Sustainable and smart rail transit based on advanced self-powered sensing technology

doi:10.1016/j.isci.2024.111306

iScience

Volume 27, Issue 12, 20 December 2024, 111306
第 27 卷第 12 期，2024 年 12 月 20 日，111306

https://doi.org/10.1016/j.isci.2024.111306 Get rights and content 获取权利和内容

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Summary 摘要

As rail transit continues to develop, expanding railway networks increase the demand for sustainable energy supply and intelligent infrastructure management. In recent years, advanced rail self-powered technology has rapidly progressed toward artificial intelligence and the internet of things (AIoT). This review primarily discusses the self-powered and self-sensing systems in rail transit, analyzing their current characteristics and innovative potentials in different scenarios. Based on this analysis, we further explore an IoT framework supported by sustainable self-powered sensing systems including device nodes, network communication, and platform deployment. Additionally, technologies about cloud computing and edge computing deployed in railway IoT enable more effective utilization. The deployed intelligent algorithms such as machine learning (ML) and deep learning (DL) can provide comprehensive monitoring, management, and maintenance in railway environments. Furthermore, this study explores research in other cross-disciplinary fields to investigate the potential of emerging technologies and analyze the trends for future development in rail transit.
随着轨道交通的不断发展，不断扩大的铁路网络增加了对可持续能源供应和智能基础设施管理的需求。近年来，先进的轨道自供电技术向人工智能和物联网（AIoT）方向快速发展。本综述主要讨论轨道交通中的自供电和自感应系统，分析其在不同场景下的现有特点和创新潜力。在此分析基础上，我们进一步探讨了可持续自供电传感系统支持的物联网框架，包括设备节点、网络通信和平台部署。此外，在铁路物联网中部署的云计算和边缘计算技术也能提高利用效率。部署的智能算法，如机器学习（ML）和深度学习（DL），可为铁路环境提供全面的监控、管理和维护。此外，本研究还探讨了其他交叉学科领域的研究，以研究新兴技术的潜力，并分析轨道交通未来发展的趋势。

Graphical abstract 图形摘要

Subject areas 学科领域

Artificial intelligence

Engineering

Mechanical engineering

Energy systems

人工智能工程机械工程能源系统

Introduction 导言

Rail transit, which includes high-speed railways, subways, light rail, and other urban rail networks, plays an essential role in daily transportation activities. The enclosed tracks of rail transport ensure safety, economic efficiency, and reliability, thereby significantly promoting inter-city economic development and reducing traffic congestion. The global railway network is expanding rapidly, with an average of 6,600 km of new lines added annually, including 3,000 km of high-speed rail in China alone.¹ However, the increasing number of railway kilometers, higher passenger speeds, and more considerable cargo tonnage, have led to a growing demand for continuous maintenance in rail transit. Nevertheless, the development of self-powered technology, the IoT, artificial intelligence (AI), and other emerging technologies offers more energy-efficient, intelligent ways to maintain railway infrastructure.
轨道交通包括高速铁路、地铁、轻轨和其他城市轨道网络，在日常交通活动中发挥着不可或缺的作用。轨道交通的封闭性确保了安全性、经济性和可靠性，从而极大地促进了城市间的经济发展，减少了交通拥堵。全球铁路网正在迅速扩张，平均每年新增线路 6600 公里，其中仅在中国就有 3000 公里的高速铁路。 ¹ 然而，铁路公里数的不断增加、客运速度的不断提高以及货运吨位的不断增加，导致对轨道交通持续维护的需求不断增长。然而，自供电技术、物联网、人工智能（AI）和其他新兴技术的发展为维护铁路基础设施提供了更节能、更智能的方法。

The rapid development of railways has led to the widespread application of large-scale signal and sensor networks in rail transport systems. However, their substantial energy requirements significantly increase overall operational costs. Monitoring sensors rely on batteries due to the lack of onboard power sources and spatial constraints. The extensive maintenance and replacement of these batteries escalate costs and environmental issues, making them unsuitable for intelligent transportation’s low-cost and sustainable energy needs.² Therefore, harnessing clean and renewable energy from the rail environment for application in railway vehicles and tracks holds immense potential.³^,⁴ During railway operations, numerous studies have shown that moving vehicles can directly provide various high-energy density, highly available, and environmentally friendly mechanical energy sources.⁵^,⁶ Collecting energy from renewable sources to supplement or replace batteries is currently the most feasible strategy for achieving long-term management of railway wireless detection devices.
铁路的快速发展促使大规模信号和传感器网络在铁路运输系统中得到广泛应用。然而，它们所需的大量能源大大增加了整体运营成本。由于缺乏车载电源和空间限制，监控传感器依赖于电池。这些电池的大量维护和更换工作增加了成本，也带来了环境问题，因此不适合智能交通对低成本和可持续能源的需求。 ² 因此，利用轨道环境中的清洁和可再生能源应用于铁路车辆和轨道具有巨大的潜力。 ³ ⁴ 在铁路运营期间，大量研究表明，行驶中的车辆可以直接提供各种高能量密度、高可用性和环保的机械能源。 ⁵ ⁶ 从可再生能源中收集能量来补充或替代电池，是目前实现铁路无线检测设备长期管理的最可行策略。

The development of intelligent railway transportation requires a reliable energy supply and increases the demand for monitoring and trackside electronic equipment.⁷ Real-time, continuous, and effective condition monitoring of railway vehicles and track environments through various sensors (optical, ultrasonic, infrared, and acoustic sensors) are key features of the gradual intelligence enhancement of railways.⁸ The current focus of railway IoT technology development primarily lies in a comprehensive IoT architecture, which includes wireless smart sensors (WSS), wireless sensor networks (WSN), and remote cloud monitoring platforms.⁹^,¹⁰ Self-powered railway IoT application have significant potential for development. For example, it no longer relies solely on single sensors but integrates installed energy harvesting devices for vehicle or environmental data collection and communication.¹¹ It achieves intelligent sensing nodes.¹² Furthermore, by examining the evolution of sensing and communication technologies within the railway IoT,¹³ sensor network development in this context will enter a new phase that encompasses wider detection ranges and more innovative management methods such as data analysis, low-power wide-area networks (LPWAN),¹⁴ and artificial intelligence (AI) technologies.
智能轨道交通的发展需要可靠的能源供应，同时也增加了对监控和轨道旁电子设备的需求。 ⁷ 通过各种传感器（光学传感器、超声波传感器、红外传感器和声学传感器）对铁路车辆和轨道环境进行实时、连续和有效的状态监测，是铁路智能化逐步提升的主要特征。 ⁸ 当前铁路物联网技术发展的重点主要在于全面的物联网架构，其中包括无线智能传感器（WSS）、无线传感器网络（WSN）和远程云监控平台。 ⁹ ¹⁰ 自供电铁路物联网应用具有巨大的发展潜力。例如，它不再仅仅依靠单一的传感器，而是集成了已安装的能量收集设备，用于车辆或环境数据的收集和通信。 ¹¹ 实现智能传感节点。 ¹² 此外，通过研究铁路物联网中传感和通信技术的演变， ¹³ 在此背景下的传感器网络发展将进入一个新阶段，包括更广泛的检测范围和更创新的管理方法，如数据分析、低功耗广域网（LPWAN）、 ¹⁴ 和人工智能（AI）技术。

With the continuous, stable, and sustainable energy supply from self-powered devices,¹⁵ intelligent algorithms deployed for optimizing and monitoring rail transit can adapt to changing environments and improve performance. Many algorithms directly process the electrical signals generated by the power supply devices, performing tasks such as recognition, classification, and anomaly detection. This enables secondary energy utilization, known as self-powered sensing. Machine learning (ML) and deep learning (DL) algorithms, which are subsets of AI, play a predominant role in this process.¹⁶ The advancements in information and communication technologies (ICT), such as 5G communication networks, have enhanced data collection rates to meet the requirements for extensive data analysis and more sophisticated AI applications.
有了来自自供电设备的持续、稳定和可持续的能源供应， ¹⁵ 用于优化和监控轨道交通的智能算法就能适应不断变化的环境并提高性能。许多算法直接处理供电设备产生的电信号，执行识别、分类和异常检测等任务。这就实现了二次能源利用，即所谓的自供电传感。作为人工智能子集的机器学习 (ML) 和深度学习 (DL) 算法在这一过程中发挥着主导作用。 ¹⁶ 信息和通信技术（ICT）（如 5G 通信网络）的进步提高了数据收集率，从而满足了大量数据分析和更复杂的人工智能应用的要求。

Since the initial exploratory stages a few years ago,¹⁷ these algorithms, surpassing traditional methods’ capabilities, have undergone evolved significantly to conduct complex and comprehensive analyses of system-acquired datasets. ML algorithms have already demonstrated successful applications in traffic flow prediction, traffic congestion modeling, and scheduling tasks such as mode selection.¹⁸^,¹⁹^,²⁰^,²¹ In rail transit, emerging evidence indicates these algorithms’ vast potential.²² Their applications include analyzing vehicle equipment conditions, detecting driver states, monitoring surrounding environment safety, optimizing traffic control, and managing energy. Additionally, emerging technologies in other fields²³ such as maglev technology, large language models (LLM), and digital twin (DT) also provide broader perspectives for the rapid development direction of large-capacity and intelligent rail transit.
自几年前的初步探索阶段以来， ¹⁷ 这些算法已经超越了传统方法的能力，在对系统获取的数据集进行复杂而全面的分析方面取得了长足的发展。ML 算法已成功应用于交通流量预测、交通拥堵建模和调度任务（如模式选择）中。 ¹⁸ ¹⁹ ²⁰ ²¹ 在轨道交通领域，新出现的证据表明了这些算法的巨大潜力。 ²² 它们的应用包括分析车辆设备状况、检测驾驶员状态、监控周围环境安全、优化交通控制和管理能源。此外，磁悬浮技术、大型语言模型 (LLM) 和数字孪生 (DT) 等其他领域的新兴技术也为大容量智能轨道交通的快速发展方向提供了更广阔的前景。

According to Industry 4.0 technologies, the integration of AI and IoT, known as AIoT, has been extensively applied in rail transit²⁴ and has become crucial for the realization of future intelligent autonomous train operations (iATO).²⁵ As shown in Figure 1, this review mainly discusses the traditional self-powered, self-sensing devices used in various rail scenarios, analyzing their characteristics, innovations, and development directions. It further conducts a hierarchical analysis of advanced technologies like the IoT, introducing the role of self-powered sensing devices in the railway IoT framework. Additionally, this review analyzes various algorithms, including modeling, ML, and DL that widely used in different rail scenarios. It also highlights their features, advantages, and innovative applications. Finally, from the perspective of emerging AIoT technologies, this review explores interdisciplinary research in this field, analyzing potential trends for future intelligent development of rail transit.
根据工业 4.0 技术，人工智能与物联网的融合（即 AIoT）已广泛应用于轨道交通 ²⁴ ，并成为实现未来智能自主列车运行（iATO）的关键。 ²⁵ 如图 1 所示，本综述主要讨论了各种轨道场景中使用的传统自供电、自感应设备，分析了它们的特点、创新和发展方向。本综述进一步对物联网等先进技术进行了分层分析，介绍了自供电传感设备在铁路物联网框架中的作用。此外，本综述还分析了广泛应用于不同铁路场景的各种算法，包括建模、ML 和 DL。本综述还重点介绍了这些算法的特点、优势和创新应用。最后，本综述从新兴人工智能物联网技术的角度，探讨了该领域的跨学科研究，分析了轨道交通未来智能化发展的潜在趋势。

Self-powered sensing technology
自供电传感技术

Self-powered devices have significantly developed, resulting in various designs and implementations widely applied in rail transit. These devices collect energy from different scenarios within this field.²⁶ As shown in Table 1, the collection methods include triboelectric nanogenerators (TENGs),²⁷^,²⁸ which convert mechanical vibrations into electrical energy through contact electrification and separation; piezoelectric nanogenerators (PENGs),²⁹^,³⁰ which utilize the property of piezoelectric materials to generate charges under mechanical stress, it is suitable for harvesting vibrational energy; and electromagnetic generators (EMGs),³¹^,³²^,³³ which rely on electromagnetic induction, generating current through the relative motion between coils and magnets; Wind turbines can also generate power by harnessing the airflow created by moving trains. Other natural energy sources can be harvested through photovoltaic cells,³⁴ pyroelectric nanogenerators,³⁵ and acoustic energy collectors.³⁶
自供电设备得到了长足的发展，各种设计和实施方法广泛应用于轨道交通领域。这些设备从该领域的不同场景中收集能量。 ²⁶ 如表 1 所示，收集方法包括三电纳米发电机（TENGs）， ²⁷ ²⁸ 通过接触电化和分离将机械振动转换为电能；压电纳米发电机（PENGs）， ²⁹ ³⁰ 利用压电材料在机械应力下产生电荷的特性，适用于收集振动能量；以及电磁发电机 (EMG)， ³¹ ³² ³³ ，它依靠电磁感应，通过线圈和磁铁之间的相对运动产生电流；风力涡轮机也可以利用火车行驶时产生的气流发电。其他自然能源可通过光伏电池、 ³⁴ 热电纳米发电机、 ³⁵ 和声能收集器收集。 ³⁶

Table 1. Comparison of several energy harvesting technologies
表 1.几种能量收集技术的比较

Empty Cell	Triboelectric nanogenerator (TENG) 三电纳米发电机（TENG）	Electromagnetic generator (EMG) 电磁发生器（EMG）	Piezoelectric (PZT) 压电（PZT）	Photovoltaic (PV) 光伏（PV）
Principles 原则	Triboelectric effect and electrostatic induction. 三电效应和静电感应	Faraday’s laws of electrolysis. 法拉第电解定律。	The asymmetric movement of charges in piezoelectric material when subjected to mechanical strain. 压电材料中的电荷在受到机械应变时的不对称运动。	Electron-hole pair generated from the semiconductor materials. 半导体材料产生的电子-空穴对。
Efficiency 效率	high area power density and energy conversion efficiency. 高区域功率密度和能量转换效率。	High conversion efficiency, high current, low resistive impedance. 转换效率高、电流大、电阻阻抗低。	High voltage and power density. 高电压和功率密度。	Depends on the materials. 取决于材料。
scalability and applicability 可扩展性和适用性	Affected by humidity and other environmental factors. Apply to most scenarios, such as irregular and low-frequency mechanical energy collection. 受湿度和其他环境因素影响。适用于大多数情况，如不规则和低频机械能收集。	Apply to most scenarios. 适用于大多数情况。	Affected by the range of working frequency. Normally being integrated to MEMS. 受工作频率范围的影响。通常与 MEMS 集成。	Affected by solar radiation and temperature. Apply to transportation equipment, household & building systems, and others. 受太阳辐射和温度影响。适用于运输设备、家用和建筑系统等。

These self-powered systems demonstrate significant potential in enhancing energy efficiency, reducing maintenance costs, and promoting green transportation in rail systems. They can be deployed in various scenarios of rail transit, sustainably powering remote monitoring devices, signaling systems, and sensor networks. It also serves as an auxiliary energy source for train operation and maintenance, thus supporting the development of intelligent transportation systems. Numerous studies have highlighted innovations in power generation modes, scenarios, and materials of these self-powered devices, marking them as critical drivers for sustainable development in rail transit.
这些自供电系统在提高能源效率、降低维护成本和促进轨道系统绿色交通方面展现出巨大潜力。它们可以部署在轨道交通的各种场景中，为远程监控设备、信号系统和传感器网络提供可持续的动力。它还可作为列车运行和维护的辅助能源，从而支持智能交通系统的发展。大量研究强调了这些自供电设备在发电模式、应用场景和材料方面的创新，将其视为轨道交通可持续发展的关键驱动力。

Currently, typical applications of energy harvesters in rail transit include collecting mechanical energy from train components such as suspensions, bogies, wheel bearings, and surrounding tracks.³⁷ Research has developed additional features for these scenarios, like energy-saving damping, condition monitoring, and road improvement.³⁸ Compared to regular railways, high-speed rail systems require more stringent maintenance and foreign object detection on the catenary system. For instance, Zheng et al. focused on the catenary system of high-speed railways, detecting foreign object intrusion to reduce energy loss and prevent damage to overhead contact lines.³⁹ Figure 2A shows Xiong et al.’s proposal to install wind barriers along the tracks, designed to address the risks that windy conditions pose to vehicles operating on high-speed rail and the challenges faced by sensors in maintaining sustainable operations. This innovative wind barrier structure not only mitigates the effects of crosswinds on trains but also harnesses wind energy to power sensors, thereby establishing a sustainable system for detecting wind speed safety within the rail environment.⁴⁰^,⁴¹ Furthermore, there are various self-powered sensor devices for monitoring and maintaining other infrastructure along train routes, such as tunnels and bridges. Figure 2B demonstrates Pan et al.’s utilization of the piston effect generated by high-speed trains passing through tunnels to collect renewable wind energy and power sensors in the remote rail scenarios.⁴² The proposed S-rotor and H-rotor collectors can efficiently capture wind energy, thereby facilitating the sustainable monitoring and maintenance of sensors in the tunnel. This approach ultimately contributes to the long-term safety of high-speed train operations. Figure 2C presents Zheng et al.’s novel tunnel WSN node self-powered system, which facilitates the real-time monitoring of the safety characteristics of tunnel structures. The system employs electromagnetic induction and piezoelectric patches to harvest wind energy. This hybrid energy collection methods enables the device to effectively capture wind energy under both high and low wind speed conditions, thereby addressing the power supply challenges faced by safety monitoring systems in subway tunnels.⁴³ For bridge structures, self-powered devices enable real-time monitoring and assessment, identifying potential issues for timely maintenance and repairs.⁴⁴ Figure 2D introduces Huang et al.’s self-driving acceleration TENG sensor on bridge cables, which is employed for real-time monitoring of infrastructures, including railway tracks on bridges. The sensor captures vibration signals and structural acceleration responses, integrating TENG technologies into structural health monitoring to facilitate continuous long-term monitoring of the bridge cable structure in real time.⁴⁵
目前，轨道交通中能量收集器的典型应用包括从悬挂装置、转向架、轮轴轴承和周围轨道等列车部件收集机械能。 ³⁷ 研究人员针对这些应用场景开发了其他功能，如节能减震、状态监测和道路改善。 ³⁸ 与普通铁路相比，高速铁路系统需要更严格的维护和对导线系统的异物检测。例如，Zheng 等人重点研究了高速铁路的导线系统，检测异物入侵以减少能量损失，防止架空接触网线路受损。 ³⁹ 图 2A 显示了 Xiong 等人提出的沿轨道安装风障的建议，该建议旨在解决大风天气对在高速铁路上运行的车辆造成的风险，以及传感器在保持可持续运营方面面临的挑战。这种创新的风障结构不仅能减轻横风对列车的影响，还能利用风能为传感器供电，从而建立一个可持续的系统来检测铁路环境中的风速安全。 ⁴⁰ ⁴¹ 此外，还有各种自供电传感器设备，用于监控和维护列车沿线的其他基础设施，如隧道和桥梁。图 2B 展示了 Pan 等人利用高速列车通过隧道时产生的活塞效应来收集可再生风能，并为偏远铁路场景中的传感器供电。 ⁴² 拟议的 S 型转子和 H 型转子收集器可以有效地收集风能，从而促进隧道内传感器的可持续监测和维护。这种方法最终有助于高速列车运行的长期安全。图 2C 展示了 Zheng 等人的新型隧道 WSN 节点自供电系统，该系统有助于实时监测隧道结构的安全特性。该系统采用电磁感应和压电贴片来收集风能。这种混合能源收集方法使该设备能够在高风速和低风速条件下有效地捕获风能，从而解决了地铁隧道安全监控系统所面临的供电难题。 ⁴³ 对于桥梁结构，自供电装置可实现实时监测和评估，找出潜在问题，以便及时维护和修理。 ⁴⁴ 图 2D 介绍了 Huang 等人在桥梁缆索上设计的自驱动加速度 TENG 传感器，该传感器可用于实时监测基础设施，包括桥梁上的铁轨。该传感器可捕捉振动信号和结构加速度响应，将 TENG 技术融入结构健康监测中，便于对桥梁缆索结构进行持续、长期的实时监测。 ⁴⁵

The diversification of application scenarios has spurred extensive research into improving materials for self-powered devices. In rail transit, developing advanced multifunctional sensing and self-powered flexible devices is an approach to practically strengthen the versatility of self-powered systems and thus overcome corresponding limitations.⁴⁶ For instance, as shown in Figure 3A, Jin et al.⁴⁷ proposed a magnetic levitation porous nanogenerator (MPNG) by improving material applications, making the self-powered device flexible and better suited for train bogies. The porous structure of the new materials provides a rough surface, enhancing the stability and sustainability of energy harvesting. Therefore, MPNG arrays supply sustainably power that enables the transmission of real-time data to other devices, achieving the railway monitoring. Moreover, inspired by the increased surface area of origami multilayer structures, Zhang et al.⁴⁸ developed a Paper TENG (P-TENG) by altering the materials, which provided an effective topology for triboelectric energy harvesting, as illustrated in Figure 3B. P-TENG can significantly improve the output performance of TENGs across a broader range of geometric configurations and in more adaptable environments for vibration energy harvesting.
应用场景的多样化促进了对改进自供电设备材料的广泛研究。在轨道交通领域，开发先进的多功能传感和自供电柔性设备是切实加强自供电系统的多功能性，从而克服相应限制的一种方法。 ⁴⁶ 例如，如图 3A 所示，Jin 等人 ⁴⁷ 通过改进材料应用，提出了一种磁悬浮多孔纳米发电机（MPNG），使自供电装置具有柔性，更适合列车转向架。新材料的多孔结构提供了一个粗糙的表面，增强了能量收集的稳定性和可持续性。因此，MPNG 阵列可持续供电，向其他设备传输实时数据，实现铁路监控。此外，受折纸多层结构表面积增大的启发，Zhang 等人 ⁴⁸ 通过改变材料开发了纸质 TENG（P-TENG），为三电能收集提供了有效的拓扑结构，如图 3B 所示。P-TENG 可在更广泛的几何配置范围和更适应振动能量收集的环境中显著提高 TENG 的输出性能。

Beyond adapting devices to application scenarios, many studies have focused on improving the energy-harvesting efficiency of these devices.⁵²^,⁵³^,⁵⁴ For example, in Figure 3C Sun⁴⁹ proposed a battery-free vibration-powered force sensing system (VFSS) which integrates structural loading, sensing, and energy harvesting. The system also utilized chemical synthesis of new materials which could sense tensile and compressive forces, to integrate strain film sensors into bearings. The proposed VFSS is able to provide real-time data for the safe operation and continuous monitoring of railway switch machine systems. Thus, material innovations also endow self-powered devices with enhanced functionality. Lin et al.,⁵⁰ as shown in Figure 3D, designed a self-powered high-frequency vibration sensor (HVS) with a layer-powder-layer structure. HVS enhances the response to a wider frequency range of track vibrations, providing diverse applications such as burst vibration detection, rail track fracture identification, automobile engine monitoring, and more. In Figure 3E, Yan et al.⁵¹ developed an electrospinning nanofiber PENG sensor on rail fasteners. The system not only harnesses rail vibration energy to achieve sustainable power supply, but also accurately assesses the tightness of rail fasteners based on their vibrational characteristics. This dual functionality provides more detection methods for maintenance and is essential for ensuring the safe operation of railway.
除了使设备适应应用场景之外，许多研究还将重点放在了提高这些设备的能量收集效率上。 ⁵² ⁵³ ⁵⁴ 例如，在图 3C 中，孙 ⁴⁹ 提出了一种无需电池的振动驱动力传感系统 (VFSS)，该系统集结构加载、传感和能量收集于一体。该系统还利用化学合成可感知拉力和压缩力的新材料，将应变薄膜传感器集成到轴承中。所提出的 VFSS 能够为铁路转辙机系统的安全运行和持续监控提供实时数据。因此，材料创新也赋予了自供电设备更强的功能。如图 3d 所示，Lin 等人设计了一种具有层-粉层结构的自供电高频振动传感器（HVS）。HVS 增强了对更宽频率范围的轨道振动的响应，可提供多种应用，如突发振动检测、轨道断裂识别、汽车发动机监测等。在图 3E 中，Yan 等人 ⁵¹ 在轨道扣件上开发了一种电纺纳米纤维 PENG 传感器。该系统不仅能利用轨道振动能量实现可持续供电，还能根据轨道扣件的振动特性准确评估其松紧度。这种双重功能为维护工作提供了更多检测方法，对确保铁路安全运行至关重要。

Despite numerous innovative studies, most current research treats self-powered devices only as power sources. The lack of versatility in self-powered devices results them unsuitable for integration, which leads to low utilization including a certain waste of space and cost when devices are not working. However, energy harvesting is a multidisciplinary technology involving materials, mechanics, electronics, and control. Cross-disciplinary research can expand the functionality of self-powered devices.⁵⁵^,⁵⁶ In particular, self-sensing applications, which utilize the voltage and current signals directly generated by self-powered devices, have been widely discussed in rail transit and other self-powered scenarios. Self-sensing applications provide intuitive and accurate condition detection of trains and their surroundings. With the support of emerging ICT, including the IoT framework and AI algorithms, the collected voltage and current data from self-sensing can be applied to create more intelligent and efficient self-powered nodes. For sustainable rail development, self-powered sensing technology must be foundational to advance the field and enable intelligent applications.
尽管进行了大量创新研究，但目前的大多数研究仅将自供电设备视为电源。自供电设备缺乏多功能性，因此不适合集成，导致利用率低，包括在设备不工作时造成一定的空间和成本浪费。然而，能量收集是一项涉及材料、机械、电子和控制的多学科技术。跨学科研究可以拓展自供电设备的功能。 ⁵⁵ ⁵⁶ 尤其是自感应应用，它利用自供电设备直接产生的电压和电流信号，在轨道交通和其他自供电应用场景中得到了广泛讨论。自感应应用可对列车及其周围环境进行直观、准确的状态检测。在新兴信息和通信技术（包括物联网框架和人工智能算法）的支持下，自感应收集的电压和电流数据可用于创建更智能、更高效的自供电节点。为了实现铁路的可持续发展，自供电传感技术必须成为推动该领域发展和实现智能应用的基础。

Advanced IOT applications
先进的物联网应用

IoT technology has developed various rail-specific applications with extensive functionalities. These applications encompass logistics, management, maintenance, monitoring, and control systems for railways,⁸^,⁴⁶ aiming to enhance the sustainability of railway management and operations.¹⁰ Sensors enabled by the development of the IoT can be deployed in any location and obtain more information and data in high quality from the sensors network. However, the expanding capacity of IoT introduces several challenges: insufficient functionality of individual sensor nodes, poor coordination between these sensors, inadequate energy supply, high operation and maintenance costs, and issues with the quality of service in wireless transmission networks.⁵⁷ Unstable and inefficient IoT components can pose significant risks and impacts on rail transit, especially concerning railway, electrical, and maintenance safety.
物联网技术已开发出各种具有广泛功能的铁路专用应用程序。这些应用包括铁路物流、管理、维护、监测和控制系统， ⁸ ⁴⁶ 旨在提高铁路管理和运营的可持续性。 ¹⁰ 物联网的发展使传感器可以部署在任何地点，并从传感器网络中获得更多高质量的信息和数据。然而，物联网不断扩大的容量也带来了一些挑战：单个传感器节点功能不足、这些传感器之间协调性差、能源供应不足、运行和维护成本高以及无线传输网络的服务质量问题。 ⁵⁷ 不稳定和低效的物联网组件会对轨道交通造成重大风险和影响，尤其是在铁路、电气和维护安全方面。

Thus, integrating previously discussed self-powered and self-sensing technologies is essential for the sustainable and green development of railway IoT.⁵⁸^,⁵⁹^,⁶⁰ Various energy harvesting and sensing technologies enable sensors or IoT applications to achieve self-sufficiency or self-support.¹¹ Liu et al.⁶¹ discussed several self-sustaining IoT systems, covering energy harvesters, sensing devices, and signal transmission systems. These devices and systems leverage technologies within extensive railway infrastructure to reduce their life cycle costs. Simultaneously, railway facilities can employ IoT applications for preventive maintenance, enhancing rail transit safety and efficiency. Yang et al.¹² described a more comprehensive IoT architecture, including the structure from the physical layer, network layer, to the application layer. This chapter will also reference such architectures, organizing the applications of various self-powered and self-sensing devices within IoT. As shown in Figure 4, the analysis will follow the composition of IoT, focusing on wireless smart sensors (WSS), wireless sensor networks (WSN), and cloud or local terminal data storage.
因此，整合前面讨论的自供电和自传感技术对于铁路物联网的可持续绿色发展至关重要。 ⁵⁸ ⁵⁹ ⁶⁰ 各种能量采集和传感技术可使传感器或物联网应用实现自给自足或自我支持。 ¹¹ Liu 等人 ⁶¹ 讨论了若干自给自足的物联网系统，包括能量收集器、传感设备和信号传输系统。这些设备和系统利用广泛的铁路基础设施内的技术来降低其生命周期成本。同时，铁路设施可利用物联网应用进行预防性维护，从而提高轨道交通的安全性和效率。Yang等人 ¹² 描述了一种更全面的物联网架构，包括从物理层、网络层到应用层的结构。本章也将参考此类架构，整理物联网中各种自供电和自传感设备的应用。如图 4 所示，本章将按照物联网的构成进行分析，重点关注无线智能传感器（WSS）、无线传感器网络（WSN）以及云端或本地终端数据存储。

Wireless smart sensors (WSS)
无线智能传感器（WSS）

Previous studies⁶²^,⁶³^,⁶⁴ have extensively discussed the composition of sensor networks in railway IoT. Wireless sensor nodes based on various sensors are the constituent units of WSN, and the basic components of the IoT. Typically, wireless sensor nodes are deployed on infrastructure or base stations and integrate multiple types of sensors, mobile computing processors, and communication modules. With the advancement of micro-electro-mechanical systems (MEMS),⁶⁵ sensor nodes within the IoT architecture for rail transit can be fully integrated with energy harvesters. These nodes can also become intelligent through self-powered and self-sensing modes, enhancing their functionality. This section will discuss self-powered WSS in rail transit, comparing and analyzing the features of sustainable, intelligent sensor nodes regarding application scenarios and functionality.
以往的研究 ⁶² ⁶³ ⁶⁴ 广泛讨论了铁路物联网中传感器网络的组成。基于各种传感器的无线传感器节点是 WSN 的组成单元，也是物联网的基本组成部分。通常，无线传感器节点部署在基础设施或基站上，集成了多种类型的传感器、移动计算处理器和通信模块。随着微机电系统 (MEMS) 的发展，轨道交通物联网架构中的 ⁶⁵ 传感器节点可以与能量收集器完全集成。这些节点还可以通过自供电和自传感模式实现智能化，从而增强其功能。本节将讨论轨道交通中的自供电 WSS，比较和分析可持续智能传感器节点在应用场景和功能方面的特点。

Adequate energy supply allows individual devices to be independently and appropriately embedded in various application scenarios. Flexible applications mean more data can be collected and monitored while rail vehicles operating. Some studies have installed wireless sensor nodes on train wheel axles.⁶⁶^,⁶⁷ For example, Gong et al.⁶⁸ embedded a variable resistor energy harvester (VREH) into train wheel bearings as WSN nodes to continuously monitor bearing conditions. Figure 5A shows nodes embedded in tracks and railway turnouts, using stress sensors to monitor structures while collecting vibration energy for wireless data transmission.⁴⁹^,⁶⁹ This setup forms an integrated WSS that collects stress, signal, and energy flows. Thus, the proposed system developed an autonomous, battery-free, and sustainable monitoring node. Wang et al.⁷⁰ embedded a WSN node into a train coupling scheme, integrating a processor, energy harvester, sensor, and signal transmitter to create an in-train forces monitoring system. As wireless sensors become more intelligent, IoT nodes must provide more functionalities beyond simple monitoring. Most self-powered WSSs unfortunately rely on data collected by individual sensors. However, integrating energy harvesters, sensors, and wireless transmission modules into a single self-powered WSS can yield more accurate data for various state detections in rail transit. Figure 5B Wu⁷¹ proposed a wireless acoustic sensor network (WASN) for rail flaw detection and localization diagnosis system. The WASN nodes rapidly sample the acoustic emission signals on the rail surface, subsequently calculating and wirelessly transmitting their characteristic parameters to the gateway for data processing. Wang et al.⁷² designed a self-sustained WSS node based on a hybridized TENG and piezoelectric energy generator (PEG) vibration mechanism. It includes a microcontroller unit (MCU) and a radio frequency (RF) transceiver for continuous monitoring of track vibration signals in harsh environments, as illustrated in Figure 5C. In the proposed self-sustainable node, the output from the TENG serves as the acceleration sensing signal, which is subsequently processed by the MCU. Meanwhile, the PEG provides a continuous power supply to both MCU and RF transceiver, enabling effective transmission of the acceleration signal. Cii et al.⁷³ shows a node powered by solar panels, designed with integrated power management, data acquisition, and wireless transmission circuits. This node transmits its generated voltage signals to the onboard control unit, synchronizing the monitoring and controlling the train’s status.
充足的能源供应可使单个设备独立、适当地嵌入各种应用场景。灵活的应用意味着可以在轨道车辆运行时收集和监控更多数据。一些研究已在列车轮轴上安装了无线传感器节点。 ⁶⁶ ⁶⁷ 例如，Gong 等人 ⁶⁸ 将可变电阻能量收集器 (VREH) 作为 WSN 节点嵌入列车轮轴，以持续监控轴承状况。图 5A 显示了嵌入轨道和铁路道岔中的节点，这些节点使用应力传感器监控结构，同时收集振动能量用于无线数据传输。 ⁴⁹ ⁶⁹ 这种设置形成了一个集成的 WSS，可收集应力、信号和能量流。因此，所提议的系统开发出了一个自主、无需电池、可持续的监测节点。Wang 等人 ⁷⁰ 将 WSN 节点嵌入到列车耦合方案中，集成了处理器、能量收集器、传感器和信号发射器，创建了列车内力监测系统。随着无线传感器变得越来越智能，物联网节点必须提供更多的功能，而不仅仅是简单的监测。遗憾的是，大多数自供电 WSS 都依赖于单个传感器收集的数据。然而，将能量收集器、传感器和无线传输模块集成到单个自供电 WSS 中，可以为轨道交通中的各种状态检测提供更准确的数据。图 5B 吴 ⁷¹ 提出了一种用于轨道探伤和定位诊断系统的无线声学传感器网络（WASN）。WASN 节点快速采样轨道表面的声发射信号，然后计算其特征参数并无线传输到网关进行数据处理。Wang 等人 ⁷² 设计了一种基于混合 TENG 和压电能量发生器 (PEG) 振动机制的自持式 WSS 节点。它包括一个微控制器单元 (MCU) 和一个射频 (RF) 收发器，用于在恶劣环境中持续监测轨道振动信号，如图 5C 所示。在拟议的自持式节点中，TENG 的输出作为加速度感应信号，随后由 MCU 进行处理。同时，PEG 为 MCU 和射频收发器提供持续供电，从而实现加速度信号的有效传输。Cii 等人 ⁷³ 展示了一个由太阳能电池板供电的节点，其设计集成了电源管理、数据采集和无线传输电路。该节点将其产生的电压信号传输到车载控制单元，同步监测和控制列车的状态。

Wireless sensor network (WSN)
无线传感器网络（WSN）

WSNs comprise spatially distributed independent sensor nodes.⁶² The dynamic nature of the environment in the rail transit necessitates the construction of more stable and secure WSNs⁷⁴ to enable unified, efficient, and intelligent monitoring and management of embedded devices. In addition to WSS’s self-sufficiency and intelligent management capabilities, communication technology advancements have expanded WSN coverage and adaptability to various operational environments along railway lines. This section will discuss the improvements, innovations, and development trends in WSNs in Industry 4.0 that provide fundamental communication functionalities within rail transit by integrating self-powered and self-sensing devices.
WSN 由空间分布式独立传感器节点组成。 ⁶² 轨道交通环境的动态特性要求构建更加稳定和安全的 WSN ⁷⁴ ，以实现对嵌入式设备的统一、高效和智能监控和管理。除了 WSS 的自给自足和智能管理能力外，通信技术的进步还扩大了 WSN 的覆盖范围，使其能够适应铁路沿线的各种运行环境。本节将讨论工业 4.0 中 WSN 的改进、创新和发展趋势，通过集成自供电和自传感设备，为轨道交通提供基本通信功能。

Li designed a⁷⁵ battery-free railway track defect monitoring system, which utilizes radio frequency energy to collect and transmit railway health status data to a remote-control center via the global system for mobile communications railway (GSM-R) communication system. Bi⁷⁶ proposed a TENG-driven self-powered sensor node that utilizes long time evolution (LTE) network communication for long-distance wireless transmission of abnormal sound signals. In addition to wide-area mobile communication networks such as GSM-R, LTE-R, and 5G-R,⁷⁷^,⁷⁸^,⁷⁹ LPWANs like long range low power radio frequency technology (LoRa) and narrowband internet of things (NB-IoT) are also commonly used for communication between rail transit sensor nodes and remote terminal servers.⁸⁰ Quevy et al. used a TENG energy harvester to power acoustic sensors.⁸¹ Considering the power limitations of energy harvesting, the focus was on the power consumption of protocols and node communication within the sensor network. LoRa networks and cloud applications were chosen for communication within embedded gateways, transmitting data about traffic signals. Gao et al.⁸² constructed an integrated sensor node that processes collected voltage data in the MCU, identifying loads and calculating damage from strain signals. Data are then transmitted via a LoRa wireless module for remote terminal monitoring of structural fatigue, as depicted in Figure 6.
Li 设计了一种 ⁷⁵ 无电池铁路轨道缺陷监测系统，该系统利用射频能量收集铁路健康状态数据，并通过全球移动通信系统铁路（GSM-R）通信系统将数据传输到远程控制中心。Bi ⁷⁶ 提出了一种由 TENG 驱动的自供电传感器节点，该节点利用长时间演进（LTE）网络通信实现异常声音信号的远距离无线传输。除了 GSM-R、LTE-R 和 5G-R 等广域移动通信网络， ⁷⁷ ⁷⁸ ⁷⁹ LPWAN（如长距离低功耗射频技术（LoRa）和窄带物联网（NB-IoT））也常用于轨道交通传感器节点与远程终端服务器之间的通信。 ⁸⁰ Quevy 等人使用 TENG 能量收集器为声学传感器供电。 ⁸¹ 考虑到能量收集的功耗限制，重点放在了传感器网络内协议和节点通信的功耗上。在嵌入式网关内的通信中选择了 LoRa 网络和云应用，以传输有关交通信号的数据。Gao 等人 ⁸² 构建了一个集成传感器节点，可在 MCU 中处理收集到的电压数据，识别负载并根据应变信号计算损坏程度。然后通过 LoRa 无线模块传输数据，对结构疲劳进行远程终端监控，如图 6 所示。

For the control of trains and local monitoring using sensor data, constructing a highly mobile vehicle ad-hoc network (VANET) for local area network communication⁸³^,⁸⁴ is essential to ensure data privacy and security. These networks are well-suited for long-term monitoring in challenging environments, as they can overcome transmission delays and data loss.⁸⁵ Bluetooth is often used as a convenient and relatively low-power short-range local communication method for point-to-point transmission of data collected by self-powered sensor nodes in rail environments.⁶⁶ Zanelli demonstrates solar panels mounted outside train carriages to power sensors and communication modules. Bluetooth low energy (BLE) boards receive data from wireless sensor nodes and are stored locally.¹³ Other short-range communication networks such as Zigbee, WIFI, and radiofrequency identification (RFID) also have widespread applications.⁸⁶^,⁸⁷^,⁸⁸ For the performance and characteristics of different communication methods, Table 2 shows several specific comparative results.
要利用传感器数据对列车进行控制和本地监控，就必须为局域网通信 ⁸³ ⁸⁴ 构建一个高度移动的车载 ad-hoc 网络 (VANET)，以确保数据的私密性和安全性。这些网络能够克服传输延迟和数据丢失问题，非常适合在具有挑战性的环境中进行长期监控。 ⁸⁵ 蓝牙通常被用作一种方便且功耗相对较低的短距离本地通信方法，用于点对点传输自供电传感器节点在轨道环境中收集的数据。 ⁶⁶ Zanelli 演示了安装在列车车厢外的太阳能电池板，用于为传感器和通信模块供电。蓝牙低能耗 (BLE) 板从无线传感器节点接收数据并存储在本地。 ¹³ Zigbee、WIFI 和射频识别 (RFID) 等其他短程通信网络也有广泛应用。 ⁸⁶ ⁸⁷ ⁸⁸ 对于不同通信方法的性能和特点，表 2 列出了几种具体的比较结果。

Table 2. Comparison of different IoT communication networks and protocols based on self-powered systems
表 2.基于自供电系统的不同物联网通信网络和协议的比较

References 参考资料	Self-powered harvesters 自供电收割机	Communication 交流	Distance 距离	Power consumption 耗电量	Application/Target 应用/目标
Wang et al.⁸⁶ Wang et al.	EMG	Zigbee	10-100 m	Very low consumption 消耗量极低	Transmit data from sensors mounted on the wheels to a monitoring terminal 从安装在车轮上的传感器向监控终端传输数据
Zanelli et al.¹³ Zanelli et al.	Photovoltaic generator 光伏发电机	Bluetooth Low Energy 5.0(BLE) 低功耗蓝牙 5.0（BLE） /4G/LTE	10-100 m	Very low consumption 消耗量极低	Build a gateway, communicate with wireless sensor nodes through BLE in LAN; Data is transmitted to a remote PC via a 4G/LTE modem within the WAN 建立一个网关，通过局域网内的 BLE 与无线传感器节点通信；通过广域网内的 4G/LTE 调制解调器将数据传输到远程 PC 上
Dziadak et al.⁶⁶ Dziadak et al.	PENG	BLE/WIFI	10-100 m	Very low/Higher consumption 消耗量极低/较高	The transmission protocol is selected according to the power generation, so that different modes of measurement data communication can be carried out 根据发电量选择传输协议，可进行不同模式的测量数据通信
He et al.⁸⁹ He et al.	EMG	Wireless Transceiver 无线收发器	10-100 m	Low consumption 低消耗	The vibration profile parameters collected by the remote data logger are transmitted for train positioning and axle safety monitoring 远程数据记录器收集的振动曲线参数被传送用于列车定位和车轴安全监控
Quevy et al.⁸¹ Quevy et al.	TENG	LoRa/NB-IoT/EnOcean/Miwi	1-10 km 1-10 公里	Very low consumption 消耗量极低	A new traffic signal control method is designed by communicating with the remote server while tracking the vehicle 通过与远程服务器通信，同时跟踪车辆，设计出一种新的交通信号控制方法
Gong et al.⁶⁸ Gong et al.	Variable reluctance energy harvester (VREH) 可变磁阻能量收集器（VREH）	Wireless Transceiver（RF）无线收发器（RF）	1-100 m	Adjustable consumption 可调节的消耗量	The recorded temperature, vibration and strain are stored on an SD card and then transmitted to a PC via RF signals for monitoring 记录的温度、振动和应变被存储在 SD 卡中，然后通过射频信号传输到 PC 进行监测
Magno et al.⁸⁸ 马格诺等人 ⁸⁸	Photovoltaic generator 光伏发电机	Bluetooth/Zigbee 蓝牙/Zigbee	2-30 m	Low consumption 低消耗	Design wireless video sensor nodes with local processing, low-power hardware, power management, and energy harvesting 设计具有本地处理、低功耗硬件、电源管理和能量收集功能的无线视频传感器节点
Li et al.⁷⁵ Li et al.	Radio-frequency energy harvester 射频能量采集器	GSM-R	1-10 km 1-10 公里	Relatively high 相对较高	The power supply and track health status are collected through RF and the railway status data is transmitted to the control center 通过射频采集供电和轨道健康状态，并将铁路状态数据传输到控制中心
Bi et al.⁷⁶ Bi et al.	PENG	LTE	1-10 km 1-10 公里	Lower than GSM, higher than short-haul communication 低于 GSM，高于短途通信	The track data is transmitted wirelessly to the remote server through LTE for foreign object detection 轨道数据通过 LTE 无线传输到远程服务器，用于异物检测
Cii et al.⁷³ Cii et al.	Photovoltaic generator 光伏发电机	Zigbee	10-100 m	Very low consumption 消耗量极低	All measured values are collected and the microprocessor properly analyzes the data. 收集所有测量值，并由微处理器对数据进行适当分析。
Sun et al.⁴⁹ Sun et al.	PENG	WIFI	100-300 m	Higher consumption 更高的消费量	The track vibration signal collected by the energy collector is transmitted wirelessly to the client through WIFI 能量收集器收集的轨道振动信号通过 WIFI 无线传输到客户端
Balid et al.⁵⁹ Balid et al.	EH Unit EH 股	Zigbee	10-100 m	Very low consumption 消耗量极低	Use the advantages of a mesh network topology, thereby eliminating a single point of failure. 利用网状网络拓扑结构的优势，从而消除单点故障。

IoT platform 物联网平台

From a technical perspective, the IoT platform is situated at the application layer of the architecture, also known as the application enablement platform (AEP). It encompasses an operable upper-layer infrastructure and provides service endpoints for all data collected by sensor nodes. With the increasing support for WSS and WSN from self-powered and self-sensing devices, a growing volume of sensor data demands enhanced device management, data security, and information regulation on the IoT platform. Automated and continuous remote monitoring significantly reduces labor and time costs. Currently, some railway self-powered IoT applications transmit data via WSN to local servers or edge devices for storage or edge computing. For example, He et al.⁹⁰ designed a self-powered railway wagon monitoring sensor which is powered by EMG technology and mounted on the carriages of freight trains and is utilized to collect monitoring data from freight cars. The data are subsequently read and analyzed by the embedded algorithm in edge device MCU, thereby facilitating the detection of axle cracks. However, considering the computational limitations and performance constraints of embedded devices, many studies transmit data over wide-area networks to remote terminals for processing, computation, and monitoring,⁹¹^,⁹² thus leveraging cloud computing applications.
从技术角度看，物联网平台位于架构的应用层，也称为应用启用平台（AEP）。它包括一个可操作的上层基础设施，并为传感器节点收集的所有数据提供服务端点。随着自供电和自传感设备对 WSS 和 WSN 的支持越来越多，传感器数据量的不断增长要求物联网平台加强设备管理、数据安全和信息监管。自动化和持续的远程监控大大降低了人力和时间成本。目前，一些铁路自供电物联网应用通过 WSN 将数据传输到本地服务器或边缘设备进行存储或边缘计算。例如，He 等人 ⁹⁰ 设计了一种自供电铁路货车监测传感器，该传感器采用肌电图技术，安装在货运列车的车厢上，用于收集货运车辆的监测数据。这些数据随后通过边缘设备 MCU 中的嵌入式算法进行读取和分析，从而帮助检测车轴裂纹。然而，考虑到嵌入式设备的计算能力和性能限制，许多研究通过广域网将数据传输到远程终端进行处理、计算和监控， ⁹¹ ⁹² 从而利用了云计算应用。

In terms of the scalability of IoT platform functionalities, both on-premises and cloud-based servers can achieve intelligent operations by incorporating data analysis or algorithmic capabilities beyond simple data monitoring. Cloud-based deployment facilitates easier application expansion through algorithmic software, and online maintenance also reduces costs,¹²^,⁹³^,⁹⁴ as depicted in Figure 7A. Gao et al. transmitted raw data to the cloud, where cloud-deployed algorithms identify foreign object intrusion and detect faults in rail transit scenarios.⁹⁵ Local deployments retain real-time solid performance and high security. Related research focuses on embedding ML algorithms in local gateways or servers to enable faster anomaly detection across various scenarios,¹³^,⁶⁷^,⁹¹ as shown in Figure 7B. Chen focused on providing various functions on edge devices, using local inference on data to detect anomalies.⁹⁶ He argues that local deployment minimizes maintenance work and data transmission costs while end-to-end inference reduces potential data security issues.
就物联网平台功能的可扩展性而言，无论是内部部署还是基于云的服务器，都可以通过集成数据分析或算法功能来实现智能操作，而不仅仅是简单的数据监控。如图 7A 所示，基于云的部署可通过算法软件更轻松地扩展应用，在线维护也可降低成本， ¹² ⁹³ ⁹⁴ 。Gao 等人将原始数据传送到云端，由云端部署的算法识别异物入侵并检测轨道交通场景中的故障。 ⁹⁵ 本地部署保留了实时可靠的性能和较高的安全性。相关研究的重点是在本地网关或服务器中嵌入 ML 算法，以便在各种场景中实现更快的异常检测， ¹³ ⁶⁷ ⁹¹ 如图 7B 所示。Chen 重点关注在边缘设备上提供各种功能，使用本地数据推理来检测异常。 ⁹⁶ 他认为，本地部署可最大限度地减少维护工作和数据传输成本，而端到端推理可减少潜在的数据安全问题。

From an architectural perspective, the IoT encompasses the physical, network, and application layers, which correspond to the discussed WSS, WSN, and IoT platform components. The use of self-powered and self-sensing devices has led to various advancements in rail transit IoT technology. Self-powered technology provides renewable energy for sensor devices in railway scenarios while self-sensing technology offers diverse types of sensor data in the rail environment. The combination of these technologies enables greener and more sustainable vehicle and environmental monitoring. However, further functional expansion in applications is necessary to meet the intelligent demands of Industry 4.0. The IoT platform has already provided an essential environment for deploying intelligent algorithms. Therefore, researching the application and development of ML and AI algorithms is crucial for achieving intelligent AIoT in rail transit IoT.
从架构的角度来看，物联网包括物理层、网络层和应用层，与所讨论的 WSS、WSN 和物联网平台组件相对应。自供电和自感应设备的使用推动了轨道交通物联网技术的各种进步。自供电技术可为铁路场景中的传感器设备提供可再生能源，而自传感技术则可在铁路环境中提供各种类型的传感器数据。这些技术的结合实现了更环保、更可持续的车辆和环境监测。然而，要满足工业 4.0 的智能需求，还需要进一步扩展应用功能。物联网平台已经为部署智能算法提供了必要的环境。因此，研究 ML 和 AI 算法的应用和发展对于在轨道交通物联网中实现智能 AIoT 至关重要。

Advanced algorithms 高级算法

Self-powered and self-sensing technologies offer promising energy solutions for rail transit. The IoT application in railways has led to more stable and diverse sensor data collection in the track environment. As a result, advanced self-powered systems demonstrate significant potential in both the front-end and back-end intelligent applications of the system.⁹⁷
自供电和自传感技术为轨道交通提供了前景广阔的能源解决方案。物联网在轨道交通中的应用使轨道环境中的传感器数据收集更加稳定和多样化。因此，先进的自供电系统在系统的前端和后端智能应用中都展现出巨大的潜力。 ⁹⁷

The evolution of algorithms has always accompanied the development of intelligent transportation and smart railways. Traditionally, data analysis and statistics were primarily handled through mathematical modeling.⁹⁸ However, with continuous algorithmic improvements and enhanced computational capabilities, an increasing number of self-powered scenarios now support the deployment of AI algorithms, including machine learning (ML) and deep learning (DL) applications.⁹⁹ ML enables computational devices to mimic human behavior, thereby automating and intelligently processing data to some extent.¹⁰⁰^,¹⁰¹ Popular ML algorithms include Support Vector Machines (SVM), Decision Trees (DT), and Random Forests. SVM is a supervised learning algorithm, that excels at solving nonlinear classification problems in small to medium-sized datasets such as anomaly detection.
算法的发展一直伴随着智能交通和智能铁路的发展。传统上，数据分析和统计主要通过数学建模来处理。 ⁹⁸ 然而，随着算法的不断改进和计算能力的增强，现在越来越多的自供电场景支持人工智能算法的部署，包括机器学习 (ML) 和深度学习 (DL) 应用。 ⁹⁹ ML 使计算设备能够模仿人类行为，从而在一定程度上实现数据处理的自动化和智能化。 ¹⁰⁰ ¹⁰¹ 流行的 ML 算法包括支持向量机 (SVM)、决策树 (DT) 和随机森林。SVM 是一种监督学习算法，擅长解决中小型数据集（如异常检测）中的非线性分类问题。

In recent years, the rapid development of DL, particularly with artificial neural networks (ANN), has brought it closer to achieving AI. Supervised learning in DL significantly enhances model training.²² For instance, convolutional neural networks (CNNs), primarily used for feature extraction in computer vision (CV), exhibit strong generalization capabilities for classification tasks across various scenarios. CNNs, with relatively simple model structures and parameters, can handle images or complex multi-channel data collected by sensors in harsh environments. Additionally, recurrent neural networks (RNNs) like long short-term memory (LSTM) and gated recurrent unit (GRU) networks excel at processing long-sequence signals, such as long-term voltage power sequence signals generated by TENG sensors deployed in the field. These RNNs capture sequential relationships and positional information in time series, addressing long-distance dependencies and accurately identifying device status changes over time. Furthermore, advanced Transformer networks, known for their success in natural language processing (NLP), also utilize self-attention mechanisms for regression prediction in general time series, enabling routine anomaly state warnings.
近年来，DL，尤其是人工神经网络（ANN）的快速发展，使其更接近实现人工智能。DL 中的监督学习大大增强了模型训练。 ²² 例如，卷积神经网络（CNN）主要用于计算机视觉（CV）中的特征提取，在各种场景的分类任务中表现出强大的泛化能力。CNN 具有相对简单的模型结构和参数，可以处理恶劣环境中传感器收集的图像或复杂的多通道数据。此外，长短期记忆（LSTM）和门控递归单元（GRU）网络等递归神经网络（RNN）擅长处理长序列信号，如部署在现场的 TENG 传感器产生的长期电压功率序列信号。这些 RNN 可捕捉时间序列中的序列关系和位置信息，处理长距离依赖关系，并准确识别设备状态随时间的变化。此外，以在自然语言处理 (NLP) 领域取得成功而闻名的先进变压器网络还利用自我注意机制对一般时间序列进行回归预测，从而实现常规异常状态警告。

Currently, the application of algorithms in rail transit research has developed a relatively mature system.¹⁶ On one hand, ML is grounded in fundamental mathematical principles, which facilitates the deployment of algorithms in a wide range of application scenarios. On the other hand, DL with its modular network architecture, can achieve more sophisticated and intelligent functionalities. Consequently, it is reasonable for the widespread application of advanced algorithms in railway transportation, including track environment monitoring, train status recognition, vehicle optimization management, and driver behavior analysis. However, research and improvement of algorithms are still in their infancy, whether for the control of rail vehicles¹⁰² or the maintenance and fault detection of infrastructure.¹⁰³ Present applications often overlook the importance of cross-disciplinary approaches. Due to the compatibility constraints within a single algorithm, many studies struggle to effectively adapt algorithms across diverse scenarios. Therefore, it is essential to consider the practical deployment of the algorithm on embedded devices, in order to more effectively overcome the challenges in real-world implementation. For instance, Chen et al.⁹⁶ proposed a low-cost system based on the self-powered sensors and tiny ML techniques. Several algorithms and trained models like RF and deep neural network (DNN) are deployed on the embedded system. The low-cost MCU can run on-device ML models at a milliwatt-level power consumption, solving problems of high power-consumption and insufficient computing memory. He also employed datasets collected under normal and abnormal track conditions to train a network, ultimately detecting various abnormal states in the track environment.
目前，算法在轨道交通研究中的应用已经形成了一个相对成熟的体系。 ¹⁶ 一方面，ML 以基本数学原理为基础，便于在广泛的应用场景中部署算法。另一方面，DL 凭借其模块化网络架构，可以实现更复杂、更智能的功能。因此，先进算法在轨道交通中的广泛应用是合理的，包括轨道环境监测、列车状态识别、车辆优化管理和驾驶员行为分析等。然而，无论是对轨道车辆 ¹⁰² 的控制，还是对基础设施的维护和故障检测，算法的研究和改进仍处于起步阶段。 ¹⁰³ 目前的应用往往忽视了跨学科方法的重要性。由于单一算法的兼容性限制，许多研究都难以在不同场景中有效调整算法。因此，必须考虑算法在嵌入式设备上的实际部署，以便更有效地克服实际实施中的挑战。例如，Chen 等人 ⁹⁶ 提出了一种基于自供电传感器和微小 ML 技术的低成本系统。嵌入式系统上部署了 RF 和深度神经网络 (DNN) 等多种算法和训练有素的模型。低成本 MCU 可以在毫瓦级功耗下运行设备上的 ML 模型，从而解决了高功耗和计算内存不足的问题。他还利用在正常和异常轨道条件下收集的数据集来训练网络，最终检测出轨道环境中的各种异常状态。

This chapter analyzes algorithm examples from various research scenarios, providing new evidence that emerging intelligent algorithms, when combined with self-powered, self-sensing technologies and IoT applications, offer significant advantages and promising prospects.
本章分析了各种研究场景中的算法实例，为新兴智能算法与自供电、自传感技术和物联网应用相结合提供了新的证据，证明其具有显著优势和广阔前景。

Anomaly detection for rail environment
铁路环境异常检测

The maintenance costs of rail transit infrastructure, which include components such as tracks, sleepers, signal controllers, and transformers, constitute a significant portion of total expenses, typically ranging from 20% to 30%.¹⁰⁴^,¹⁰⁵ The application of algorithms is particularly important in reducing the human and material resources required for track maintenance and management. By deploying intelligent algorithms in self-powered systems, continuous operation can be achieved, enabling effective monitoring and preventive maintenance of infrastructure across various scenarios.¹⁰⁶
轨道交通基础设施的维护成本包括轨道、枕木、信号控制器和变压器等组件，占总支出的很大一部分，通常在 20% 到 30% 之间。 ¹⁰⁴ ¹⁰⁵ 算法的应用对于减少轨道维护和管理所需的人力和物力资源尤为重要。通过在自供电系统中部署智能算法，可实现连续运行，从而在各种情况下对基础设施进行有效监控和预防性维护。 ¹⁰⁶

Common monitoring and maintenance measures include detecting defects or cracks in the tracks and monitoring for foreign object intrusion or anomalies on the tracks. Table 3 presents examples of self-powered, self-sensing methods and algorithm types used in different environmental anomaly detection scenarios.
常见的监测和维护措施包括检测轨道上的缺陷或裂缝，以及监测轨道上的异物入侵或异常情况。表 3 举例说明了用于不同环境异常检测场景的自供电、自传感方法和算法类型。

Table 3. Algorithms and their features on self-powered devices
表 3.自供电设备上的算法及其特点

References 参考资料	Self-powered 自供电	Self-sensing 自感应	Algorithm 算法	Features of Algorithm 算法特点	Application 应用
Lu et al.⁶⁹ Lu et al.	EMG	__	Littlewood-Paley wavelet analysis Littlewood-Paley 小波分析	For processing non-stationary signals and can provide both time and frequency information 用于处理非稳态信号，可同时提供时间和频率信息	health monitoring of urban rail corrugation 城市轨道交通波纹管的健康监测
Meng et al.⁵¹ Meng et al.	TENG	Voltages 电压	Features analysis of original voltage signals 原始电压信号的特征分析	Extract key features of the voltage signal, such as amplitude, frequency, and phase 提取电压信号的主要特征，如振幅、频率和相位	Rail fasteners tightness safety detection 轨道紧固件松紧度安全检测
Alavi et al.¹⁰⁷ Alavi et al.	PENG	Voltages 电压	Three-dimensional finite element model analysis 三维有限元模型分析	Complex three-dimensional physics problems are simulated numerically 对复杂的三维物理问题进行数值模拟	Road Detection 道路检测
Bi et al.⁷⁶ Bi et al.	TENG	__	Analysis of sensor capacitance changes resulting from by the sound signals 分析声音信号导致的传感器电容变化	Measure the effect of sound signal on sensor capacitance 测量声音信号对传感器电容的影响	Railway foreign matter intrusion detection 铁路异物入侵检测
Magno et al.⁸⁸ 马格诺等人 ⁸⁸	Photovoltaic generator 光伏发电机	__	CV image processing: Background subtraction CV 图像处理：背景减影	Remove the static background from the video frame to highlight moving objects 移除视频帧中的静态背景，突出移动物体	Abandoned/Removed object detection 废弃/移除物体检测
Chellaswamy et al.¹⁰⁸ Chellaswamy et al.	__	__	Particle Swarm Optimization(PSO) 粒子群优化（PSO）	An optimization algorithm based on swarm intelligence 基于蜂群智能的优化算法	Railway track irregularity detection 铁路轨道不规则检测
Sun et al.¹⁰⁹ Sun et al.	PENG	Voltages 电压	Multi-stable phase trajectories and wavelet analysis 多稳相位轨迹和小波分析	The behavior trajectories of the system in different stable states are analyzed for nonlinear dynamic system analysis 分析系统在不同稳定状态下的行为轨迹，进行非线性动态系统分析	Rail corrugation detection 轨道波纹检测
Wang et al.⁷ Wang et al.	EMG	Voltages& Electromotive forces 电压和电动势	Short Time Fourier Transform (STFT) analysis 短时傅立叶变换 (STFT) 分析	Fourier transform is applied to the local time period of the signal 对信号的局部时间段进行傅里叶变换	Rail corrugation detection 轨道波纹检测
Sun et al.¹¹⁰ Sun et al.	EMG	Voltages& Electromotive forces 电压和电动势	Time-frequency wavelet analysis 时频小波分析	The combination of time and frequency analysis provides time-varying frequency characteristics of the signal 时间分析和频率分析相结合，可提供信号的时变频率特性	Rail corrugation detection 轨道波纹检测
Wang et al.¹¹¹ Wang et al.	EMG	__	Stack Denoising Autoencoder (SDA) 堆栈去噪自动编码器（SDA）	Multiple Denoising Auto encoders are stacked together to form a deep architecture 多个去噪自动编码器堆叠在一起，形成一个深度架构	Transformer fault detection 变压器故障检测
Kamakshi et al.⁸⁴ 卡马克西等人 ⁸⁴	PENG	__	Collision Avoidance Algorithm 避免碰撞算法	Distance measurement and threshold crossing 距离测量和阈值跨越	Train collision prevention 防止火车相撞

Given that vibration signals from mechanical equipment are typically nonlinear and non-stationary, most of the studies in Table 3 utilize time-frequency analysis methods such as Fast Fourier Transform (FFT) or wavelet analysis to extract signal characteristics.⁷^,⁶⁹^,¹¹⁰ FFT is suitable for quickly and efficiently obtaining frequency distribution information of signals, especially for stationary or transient signal analysis. For example, in,¹⁰⁹ a low-pass Butterworth filter was used in a MATLAB program to obtain real-time time-domain induced voltage from energy-harvesting sensors and FFT was applied to acquire spectral characteristics. Additionally, wavelet time-frequency transformation of acceleration data from corrugated and non-corrugated rails was performed to compare sensitive mutation points in wavelet spectrograms, thereby determining the health status of the tracks.
鉴于机械设备的振动信号通常是非线性和非稳态的，表 3 中的大多数研究都采用了时频分析方法，如快速傅立叶变换 (FFT) 或小波分析来提取信号特征。 ⁷ ⁶⁹ ¹¹⁰ FFT 适用于快速有效地获取信号的频率分布信息，尤其适用于静态或瞬态信号分析。例如， ¹⁰⁹ 在 MATLAB 程序中使用了低通巴特沃斯滤波器来获取能量收集传感器的实时时域感应电压，并应用 FFT 来获取频谱特征。此外，还对波纹钢轨和非波纹钢轨的加速度数据进行了小波时频变换，以比较小波频谱图中的敏感突变点，从而确定轨道的健康状况。

Different types of DL can be adapted to various functions. In image data processing, CNNs are primarily used for feature extraction in computer vision (CV), demonstrate strong generalization capabilities for classification tasks across different scenarios. Zheng et al. proposed a spatiotemporal enhanced (STE) CNN, which emphasizes spatial responses of foreign objects through feature-level spatiotemporal coherence, facilitating preventive maintenance of high-speed railway catenary systems.³⁹ Thus, neural networks can effectively extract features from images for learning and recognition, achieving more precise track detection.¹¹²
不同类型的 DL 可以适应各种功能。在图像数据处理中，CNN 主要用于计算机视觉（CV）中的特征提取，在不同场景的分类任务中表现出强大的泛化能力。Zheng等人提出了一种时空增强型（STE）CNN，通过特征级时空一致性强调异物的空间响应，有助于高速铁路导管系统的预防性维护。 ³⁹ 因此，神经网络可以有效地从图像中提取特征进行学习和识别，从而实现更精确的轨道检测。 ¹¹²

However, when processing collected signals, external sensors are always susceptible to environmental interference factors such as weather and structural changes in facilities. Therefore, utilizing self-sensing signals generated by self-powered devices, such as voltage or power time series, is also feasible. Since track infrastructure directly impacts railway safety, integrating all functions into self-powered devices can save physical space for sensor installation. Moreover, deploying algorithm applications solely through self-powered and self-sensing equipment can avoid the impact of intrusive devices on train operations and the regular maintenance of surrounding track infrastructure.
然而，在处理收集到的信号时，外部传感器总是容易受到天气和设施结构变化等环境因素的干扰。因此，利用自供电设备产生的自感应信号（如电压或功率时间序列）也是可行的。由于轨道基础设施直接影响铁路安全，将所有功能集成到自供电设备中可节省安装传感器的物理空间。此外，仅通过自供电和自传感设备部署算法应用，可避免侵入性设备对列车运行和周围轨道基础设施定期维护的影响。

In addition to tracking infrastructure, numerous algorithmic studies have been deployed in self-powered systems to detect other railway infrastructure. For example, Wang et al.¹¹¹ utilized EMG and employed stacked denoising autoencoder (SDA) algorithms to reconstruct voltage data affected by noise and obtain robust feature representations. The SDA involves unsupervised pre-training and supervised fine-tuning processes, using trained data to detect transformer faults in track environments. Various environmental monitoring methods are also available for self-powered self-sensing devices installed on bridges.¹¹³ In Figure 8, Zhang et al.¹¹⁴ used three-dimensional finite element analysis and LSTM algorithms to detect bridge structures traversed by railway lines, so did Alavi et al.¹¹⁵ Additionally, in specific scenarios such as stations where multiple trains require anti-collision measures, several studies have employed methods such as Balise modeling,¹¹⁶ linear fitting,¹¹⁷ CNN,¹¹⁸ and collision algorithms⁸⁴ to identify vehicle information and perceive train positions, thus achieving collision avoidance around the train bodies.
除了跟踪基础设施外，还在自供电系统中部署了大量算法研究，以检测其他铁路基础设施。例如，Wang 等人 ¹¹¹ 利用 EMG 和堆叠去噪自编码器 (SDA) 算法来重建受噪声影响的电压数据，并获得稳健的特征表示。SDA 包括无监督预训练和有监督微调过程，利用训练数据检测轨道环境中的变压器故障。对于安装在桥梁上的自供电自感应设备，也有各种环境监测方法。 ¹¹³ 在图 8 中，Zhang 等人 ¹¹⁴ 使用三维有限元分析和 LSTM 算法来检测铁路线穿越的桥梁结构，Alavi 等人也是如此。 ¹¹⁵ 此外，在车站等需要采取防碰撞措施的特定场景中，一些研究采用了 Balise 建模、 ¹¹⁶ 线性拟合、 ¹¹⁷ CNN、 ¹¹⁸ 和碰撞算法 ⁸⁴ 等方法来识别车辆信息和感知列车位置，从而实现在列车车身周围避免碰撞。

State detection of rail vehicles
轨道车辆的状态检测

Despite significant advancements in monitoring systems for rail transit, other systems for the condition of rail vehicles like freight trains, still need to meet the required performance levels.¹²⁰ Due to the complexity of train equipment, train accidents are often attributed to the wear and fatigue of vehicle components.¹¹⁴ Real-time continuous monitoring of train conditions can detect component failures early, enhancing rail transit’s efficiency and safety. To avoid disrupting the existing equipment layout, many studies propose utilizing self-powered devices to improve the overall reliability of detection systems. By incorporating algorithms to further utilize sensor data from various components, a more rational analysis of real-time data from different parts of the train can be achieved.
尽管轨道交通监控系统取得了重大进展，但货运列车等其他轨道车辆状况监控系统仍需达到所需的性能水平。 ¹²⁰ 由于列车设备的复杂性，列车事故通常归因于车辆部件的磨损和疲劳。 ¹¹⁴ 对列车状况的实时连续监控可以及早发现部件故障，从而提高轨道交通的效率和安全性。为避免破坏现有设备布局，许多研究建议利用自供电设备来提高检测系统的整体可靠性。通过采用算法进一步利用来自不同组件的传感器数据，可以对来自列车不同部分的实时数据进行更合理的分析。

As previously mentioned, bogies, axles, and wheels generate substantial mechanical energy during operation which can be harvested, shown in Figure 9A. However, these critical and frequently worn components are the focus of health monitoring and stability testing. The train bogie serves as the sole connection between the train body and the tracks and plays a crucial role in ensuring the safe operation of trains. For example, He et al.⁸⁹ analyzed voltage signals generated by an energy management structure (EMI) based on EMG, using strategic algorithms to detect axle cracks. Furthermore, some studies have installed resonant electromagnetic vibration energy harvesters (REVEHs) on bogies. These systems collect and analyze vibration information to provide real-time diagnostics and monitoring of freight trains.¹²¹ Huang et al.¹²² utilized a one-dimensional CNN (1-D-CNN) to extract features from collected vibration signals, achieving effective fault classification and fault component localization. In addition to CNNs, LSTM algorithms can also analyze and predict data sequences generated during train operation, such as bearing wear patterns or vibration signals. In Figure 9B, Fang et al.¹²³ deployed an LSTM network within a self-powered device in the bearings to detect abnormalities and assess the stability of the axles. LSTMs also help predict maintenance times, reducing unexpected downtime and enhancing transportation efficiency and safety. Other studies¹²⁴^,¹²⁵ have analyzed self-powered signals from axles for monitoring purposes in smart urban transportation scenarios. As the direct contact between train wheels and tracks, they are subject to frequent wear and tear. Therefore, it is equally important to monitor the condition of the wheels and their bearings.¹²⁶ Bernal et al.¹²⁷ proposed a fault detection system based on analog signal processing to identify flat defects in wheels using simulated acceleration signals. This approach reduces memory requirements and power consumption. Other studies have utilized time-frequency analysis of signals from self-powered devices near the wheels for monitoring and predicting wheel conditions.⁹²^,⁹⁵^,¹²⁸ gathered self-sensing vibration signals and analyzed their correlation with noise data to detect dynamic behaviors of trains, such as anomalies caused by wheel defects.
如前所述，转向架、车轴和车轮在运行过程中会产生大量机械能，这些能量可以被收集起来，如图 9A 所示。然而，这些关键且经常磨损的部件是健康监测和稳定性测试的重点。列车转向架是车体与轨道之间的唯一连接装置，在确保列车安全运行方面发挥着至关重要的作用。例如，He 等人 ⁸⁹ 分析了基于 EMG 的能量管理结构 (EMI) 产生的电压信号，利用策略算法检测车轴裂纹。此外，一些研究还在转向架上安装了共振电磁振动能量收集器（REVEHs）。这些系统收集并分析振动信息，为货运列车提供实时诊断和监控。 ¹²¹ Huang 等人 ¹²² 利用一维 CNN（1-D-CNN）从收集到的振动信号中提取特征，实现了有效的故障分类和故障部件定位。除了 CNN 之外，LSTM 算法还可以分析和预测列车运行过程中产生的数据序列，例如轴承磨损模式或振动信号。在图 9B 中，Fang 等人 ¹²³ 在轴承中的自供电装置内部署了一个 LSTM 网络，用于检测异常并评估车轴的稳定性。LSTM 还有助于预测维护时间，减少意外停机时间，提高运输效率和安全性。其他研究 ¹²⁴ ¹²⁵ 分析了车轴的自供电信号，用于智能城市交通场景中的监控目的。车轴是火车车轮和轨道之间的直接接触面，经常受到磨损。因此，监测车轮及其轴承的状况同样重要。 ¹²⁶ Bernal等人 ¹²⁷ 提出了一种基于模拟信号处理的故障检测系统，利用模拟加速度信号识别车轮的平面缺陷。这种方法降低了内存要求和功耗。其他研究利用对车轮附近自供电设备发出的信号进行时频分析，以监测和预测车轮状况。 ⁹² ⁹⁵ ¹²⁸ 收集自感应振动信号，分析其与噪声数据的相关性，以检测列车的动态行为，如车轮缺陷引起的异常。

In addition to monitoring specific components, other data related to rail vehicles should also be focused. For instance, Zanelli et al.¹³ utilized solar power on freight train bodies to intelligently measure pressure values at specific points in the system, including monitoring pressure in the main pipeline and brake cylinders to ensure safety. Monitoring the power generation efficiency of self-powered devices during train operation is also crucial. Some studies have employed modeling and DNN algorithms to predict onboard devices’ voltage and power generation, indirectly analyzing train operating conditions.¹²⁹^,¹³⁰ Tian et al.¹³¹ used the LSTM network with good coupling and high prediction accuracy to predict the load of photovoltaic power generation, thereby improving the stability of integrating photovoltaic power into rail transit systems. In intelligent transportation scenarios, research on self-powered and self-sensing devices and algorithms for vehicle condition monitoring is also evolving. These systems are becoming increasingly comprehensive, employing time-frequency analysis and DL algorithms to monitor various data types such as speed, acceleration, and flow.⁶⁷^,¹³²^,¹³³ Due to commonalities in the transportation industry, these improved algorithms also apply to rail vehicles to some extent,¹³⁴ enabling effective real-time monitoring of various vehicle parameters and achieving intelligent applications across different scenarios.
除了监控特定组件外，还应关注与轨道车辆相关的其他数据。例如，Zanelli 等人 ¹³ 利用货运列车车体上的太阳能来智能测量系统中特定点的压力值，包括监测主管道和制动缸中的压力，以确保安全。监控列车运行期间自供电设备的发电效率也至关重要。一些研究采用建模和 DNN 算法来预测车载设备的电压和发电量，从而间接分析列车运行状况。 ¹²⁹ ¹³⁰ Tian 等人 ¹³¹ 利用耦合性好、预测精度高的 LSTM 网络预测光伏发电的负载，从而提高了光伏发电融入轨道交通系统的稳定性。在智能交通领域，自供电和自传感设备以及车辆状态监测算法的研究也在不断发展。这些系统正变得越来越全面，采用时频分析和 DL 算法来监控各种数据类型，如速度、加速度和流量。 ⁶⁷ ¹³² ¹³³ 由于交通行业的共性，这些改进的算法在一定程度上也适用于轨道车辆， ¹³⁴ 实现了对各种车辆参数的有效实时监控，并在不同场景中实现了智能应用。

Behavior analysis of train drivers
火车司机行为分析

In addition to the physical factors of onboard train systems and surrounding infrastructure, human factors such as the driver’s state and behavior also significantly impact train operation safety. Driver fatigue and distraction are particularly influential, accounting for 36% of road traffic accidents.¹³⁵^,¹³⁶ Li et al.’s assessment of human-machine trust plays a crucial role in developing intelligent transportation automation in high-speed rail automation.¹³⁷ With algorithm advancements, sophisticated human-machine collaborative driving systems typically analyze the driver’s physiological signals to establish dynamic intelligent management modes, thereby enabling intelligent train operation.
除了车载列车系统和周围基础设施的物理因素外，驾驶员的状态和行为等人为因素也会对列车运行安全产生重大影响。驾驶员疲劳和分心的影响尤其大，占道路交通事故的 36%。 ¹³⁵ ¹³⁶ Li 等人对人机信任的评估在高速铁路自动化中的智能交通自动化发展中起着至关重要的作用。 ¹³⁷ 随着算法的进步，先进的人机协同驾驶系统通常会分析驾驶员的生理信号，建立动态智能管理模式，从而实现列车的智能化运行。

Like other onboard sensors, self-powered and self-sensing research can be applied to various facilities within the driver’s cabin. However, non-contact sensors such as cameras and infrared cameras often require external power sources and are susceptible to environmental influences. Direct-contact devices with self-powered and self-sensing applications are more suitable instead. A common approach is to mount TENG sensors on the steering wheel to collect energy and signals.¹³⁸ Chen et al.¹³⁹ utilized a multilayer perceptron (MLP) to extract features from voltage sequence signals, indirectly identifying and classifying driver behaviors. Additionally, the brake pedal which is operated for extended periods, also holds potential for energy harvesting and information collection. Some studies¹⁴⁰^,¹⁴¹ have mounted TENG sensors on moving brake pedals and used FFT algorithms to analyze driver behavior, shown in Figure 10A. Zhang et al.¹⁴² proposed a driver training assistance system (DTAS) which integrates three triboelectric driving operation sensors, including gear shift sensor, steering angle sensor, and pedal sensors. He also employed a CNN network, which is particularly effective at extracting subtle features from time-series data, to learn signals generated by the brake pedal, detecting and analyzing drivers' driving habits.
与其他车载传感器一样，自供电和自感应研究可应用于驾驶室内的各种设施。然而，摄像头和红外摄像机等非接触式传感器通常需要外接电源，且易受环境影响。相反，具有自供电和自传感功能的直接接触式设备更为合适。一种常见的方法是在方向盘上安装 TENG 传感器，以收集能量和信号。 ¹³⁸ Chen等人 ¹³⁹ 利用多层感知器（MLP）从电压序列信号中提取特征，间接识别驾驶员行为并对其进行分类。此外，长时间操作的制动踏板也具有能量采集和信息收集的潜力。一些研究 ¹⁴⁰ ¹⁴¹ 将 TENG 传感器安装在移动的制动踏板上，并使用 FFT 算法分析驾驶员行为，如图 10A 所示。Zhang 等人 ¹⁴² 提出了一种驾驶员培训辅助系统（DTAS），该系统集成了三个三电驾驶操作传感器，包括换挡传感器、转向角传感器和踏板传感器。他还采用了在从时间序列数据中提取微妙特征方面特别有效的 CNN 网络来学习制动踏板产生的信号，从而检测和分析驾驶员的驾驶习惯。

However, indirect signal collection through other facilities cannot accurately monitor the driver’s physiological signals. The development trends of advanced smart transportation emphasize the importance of incorporating human factors engineering in vehicle ergonomics. Human factors engineering provides interdisciplinary perspectives for developing driver monitoring systems, including wearable devices and ML algorithms, to enhance safety and comfort.¹⁴⁵ Luo et al.¹⁴³ designed a green, stretchable triboelectric sensor mounted on a wearable neck ring shown in Figure 10B. The neck ring was placed in safety to collect signals generated by neck muscle movements, including turning the head, coughing, speaking. With K-nearest neighbor (KNN), SVM, and CNN algorithms, the smart neck ring also classified movements to detect driver fatigue and other dangerous driving behaviors. In Figure 10C, Chen et al. and Yang et al.¹⁴⁴^,¹⁴⁶ both designed wearable wristband devices as energy harvesters and signal collectors. Chen et al.’s approach involved training self-sensing signals with a multi-scale RCNN algorithm, resulting in accurate recognition of driver behaviors and enhancing the reliability of human-machine interaction in driving systems. New research indicates that there are other effective methods for monitoring physiological signals that minimize discomfort to the driver. Zhang et al.¹⁴⁷ employed a ring device using an LSTM algorithm to recognize driver gestures and actions. It demonstrates the practical application of human factors engineering in self-powered and self-sensing wearable devices for transportation scenarios.
然而，通过其他设施进行的间接信号采集无法准确监测驾驶员的生理信号。先进智能交通的发展趋势强调了将人因工程学纳入车辆人体工程学的重要性。人因工程学为开发驾驶员监测系统（包括可穿戴设备和 ML 算法）提供了跨学科的视角，以提高安全性和舒适性。 ¹⁴⁵ Luo 等人 ¹⁴³ 设计了一种安装在可穿戴颈圈上的绿色、可拉伸三电传感器，如图 10B 所示。将颈环置于安全位置，可收集颈部肌肉运动（包括转头、咳嗽和说话）产生的信号。通过 K-近邻（KNN）、SVM 和 CNN 算法，智能颈环还能对动作进行分类，以检测驾驶员疲劳和其他危险驾驶行为。在图 10C 中，Chen 等人和 Yang 等人 ¹⁴⁴ ¹⁴⁶ 都设计了作为能量收集器和信号收集器的可穿戴腕带设备。Chen 等人的方法包括使用多尺度 RCNN 算法训练自感信号，从而准确识别驾驶员行为，提高驾驶系统中人机交互的可靠性。新的研究表明，还有其他有效的生理信号监测方法，可以最大限度地减少驾驶员的不适感。Zhang 等人 ¹⁴⁷ 采用了一种使用 LSTM 算法的环形装置来识别驾驶员的手势和动作。它展示了人因工程学在交通场景中自供电和自传感可穿戴设备中的实际应用。

Optimization and control of rail transit devices
轨道交通设备的优化与控制

In addition to passively recording sensor data from various rail transit scenarios, self-powered devices should actively participate in optimizing and controlling trains and other infrastructure.¹⁴⁸ utilized the gray wolf optimization and particle swarm optimization (GWO-PSO) algorithms to develop finite element analysis models for optimizing self-powered devices mounted on freight train suspension dampers. A vehicle-track coupling model was established to predict more accurate vibration responses and enhance the vibration energy harvesting efficiency of the train suspension during operation. Similarly, Fu et al. applied FFT analysis to sensor data collected from vibration energy harvesters on train bogies.¹⁴⁹ They revealed changes in the main vibration frequency in both the time and frequency domains to determine the optimal vibration frequency of the devices. Lopes et al.⁷⁰ employed an interactive adaptive weighted genetic algorithm (I-AWGA) to ascertain the optimal geometric shape of the devices, capitalizing on the algorithm’s adaptability, parallelism, and global search capability. These studies aim to maximize energy harvesting through algorithmic design.
除了被动记录来自各种轨道交通场景的传感器数据外，自供电设备还应积极参与列车和其他基础设施的优化和控制。 ¹⁴⁸ 利用灰狼优化和粒子群优化（GWO-PSO）算法开发了有限元分析模型，用于优化安装在货运列车悬挂减震器上的自供电设备。他们建立了一个车辆-轨道耦合模型，以预测更精确的振动响应，并提高列车悬挂系统在运行过程中的振动能量收集效率。同样，Fu 等人将 FFT 分析应用于从列车转向架上的振动能量收集器收集的传感器数据。 ¹⁴⁹ 他们揭示了主振动频率在时域和频域上的变化，从而确定了设备的最佳振动频率。Lopes 等人 ⁷⁰ 利用交互式自适应加权遗传算法（I-AWGA）确定了装置的最佳几何形状，充分利用了算法的适应性、并行性和全局搜索能力。这些研究旨在通过算法设计最大限度地收集能量。

In optimizing train control systems during operation, Zuo et al.¹⁵⁰ developed an electro-mechanical coupling model based on self-powered PENG to predict performance under varying load conditions and train speeds shown in Figure 11. This model was subsequently utilized to control train braking. Guo et al.¹⁵¹ proposed a promising speed-sensing and braking monitoring solution. It employs control algorithms to manage the electronic control unit (ECU) for speed control. Advanced intelligent control systems are crucial in ensuring vehicle safety at critical moments.
在运行期间优化列车控制系统时，Zuo 等人 ¹⁵⁰ 开发了基于自供电 PENG 的机电耦合模型，以预测图 11 所示的不同负载条件和列车速度下的性能。该模型随后被用于控制列车制动。Guo 等人 ¹⁵¹ 提出了一种很有前景的速度感应和制动监控解决方案。它采用控制算法来管理电子控制单元 (ECU)，以实现速度控制。先进的智能控制系统对于确保关键时刻的车辆安全至关重要。

In addition to device and control optimization, algorithms also play a role in the structural optimization of trains. For instance, Lian et al.¹⁵² utilized LSTM models to characterize the nonlinear functions of data relationships within the system. This enabled adaptive parameter control of the high-speed railway subgrade vibratory rolling compaction process for enhanced structural stability. Sun et al.¹⁵³ developed a mathematical model for the suspension system of medium- and low-speed maglev trains by employing an improved a priori algorithm to analyze stored historical data and determine adaptive fuzzy rules for the maglev train suspension system.
除了设备和控制优化，算法在列车结构优化中也发挥着作用。例如，Lian 等人 ¹⁵² 利用 LSTM 模型来描述系统内数据关系的非线性函数。这使得高速铁路路基振动碾压过程的自适应参数控制成为可能，从而增强了结构稳定性。Sun 等人 ¹⁵³ 通过采用改进的先验算法分析存储的历史数据并确定磁悬浮列车悬挂系统的自适应模糊规则，建立了中低速磁悬浮列车悬挂系统的数学模型。

Energy management of rail transit devices
轨道交通设备的能源管理

The energy requirements of general sensors can be adequately met due to the development of self-powered devices. However, the excess energy collected necessitates self-powered devices to possess energy storage and management capabilities. Some studies have utilized control strategies¹⁵⁴ and frequency domain analysis¹⁵⁵ to manage the power flow of self-powered devices effectively, achieving energy balance. Morais et al.¹⁵⁶ optimized the efficiency of charging controllers in railway transportation systems using fuzzy logic control and genetic algorithm metaheuristics to adjust fuzzy rule weights automatically. Intelligent energy management is essential in long-term rail transit operations, especially when energy harvesters temporarily cease functioning.
由于自供电设备的发展，一般传感器的能源需求可以得到充分满足。然而，由于收集的能量过剩，自供电设备必须具备能量存储和管理功能。一些研究利用控制策略 ¹⁵⁴ 和频域分析 ¹⁵⁵ 来有效管理自供电设备的功率流，从而实现能量平衡。Morais 等人 ¹⁵⁶ 利用模糊逻辑控制和遗传算法元启发式自动调整模糊规则权重，优化了铁路运输系统中充电控制器的效率。智能能源管理对轨道交通的长期运营至关重要，尤其是当能量收集器暂时停止工作时。

Furthermore, for increasingly intelligent power grid systems, the transmission of surplus energy from rail transit to smart grids for unified algorithmic management can comprehensively monitor grid status and operating conditions.¹⁵⁷ Shown in Figures 12A–12C, Liao et al. applied CNN algorithm to accurately demodulate the high-frequency carrier signals transferred in the high-voltage line.¹⁵⁸ In rail environments with distributed self-powered systems, using microgrids and distributed grids can enhance the power system’s resilience, effectively addressing potential issues such as sudden power outages.¹⁵⁹
此外，对于智能化程度越来越高的电网系统，将轨道交通的剩余能量传输到智能电网，进行统一的算法管理，可以全面监控电网状态和运行状况。 ¹⁵⁷ 如图 12A-12C 所示，Liao 等人应用 CNN 算法对高压线路中传输的高频载波信号进行了精确解调。 ¹⁵⁸ 在采用分布式自供电系统的轨道环境中，使用微电网和分布式电网可以增强电力系统的弹性，有效解决突然断电等潜在问题。 ¹⁵⁹

With the advancement of self-powered sensors in rail transit, it is highly logical to leverage ML algorithms to utilize self-sensing data. Whether monitoring railway environments or vehicle conditions, these algorithms can enhance detection accuracy and add new functionalities. Such improvements support the intelligent development of rail transit under the industry 4.0 framework. Moreover, optimization algorithms for devices and energy enhance the adaptability and robustness of self-powered devices, ensuring sustainable operation and providing a foundation for green railways. Similar to autonomous driving technology on roads, human factors engineering research using self-powered self-sensing technology for train drivers also contributes to automation in rail transit.
随着轨道交通自供电传感器的发展，利用 ML 算法来利用自传感数据是非常合理的。无论是监测铁路环境还是车辆状况，这些算法都能提高检测精度并增加新的功能。这些改进为工业 4.0 框架下的轨道交通智能化发展提供了支持。此外，针对设备和能源的优化算法可增强自供电设备的适应性和稳健性，确保可持续运行，为绿色铁路奠定基础。与公路上的自动驾驶技术类似，针对列车驾驶员的自供电自感应技术的人因工程研究也有助于轨道交通的自动化。

There are still many other algorithms active in rail transit. The transfer learning of DL has especially facilitated the application of advanced algorithms in cross-disciplinary fields such as self-powered and self-sensing technology. As discussed in this section, self-powered devices can harness abundant renewable energy in rail transit scenarios, and utilize algorithms integrated with IoT technology to document the energy harvesting process. Cross-disciplinary applications can effectively leverage all available information in these scenarios, providing ample data and foundational algorithm logic for the system’s intelligence. The utilization of algorithms meets the data-driven complex detection and decision-making requirements of Industry 4.0¹⁶⁰ and showcases the integration of other emerging technologies in rail transit.
还有许多其他算法活跃在轨道交通领域。DL 的迁移学习尤其促进了先进算法在自供电和自感应技术等交叉学科领域的应用。正如本节所讨论的，自供电设备可在轨道交通场景中利用丰富的可再生能源，并利用与物联网技术相结合的算法来记录能量采集过程。跨学科应用可有效利用这些场景中的所有可用信息，为系统智能提供充足的数据和基础算法逻辑。算法的使用满足了工业 4.0 ¹⁶⁰ 中数据驱动的复杂检测和决策要求，并展示了轨道交通中其他新兴技术的集成。

Opportunities for emerging technologies
新兴技术的机遇

In addition to addressing the smart development needs of Industry 4.0 in AIoT and AI algorithms, Industry 5.0 introduces a human-centric concept that emphasizes technology development, prioritizing human well-being, social progress, and environmental sustainability. This section focuses on emerging technologies in rail transit, including advancements in human-machine interaction, bidirectional analysis of digital-physical spaces, and improvements toward high-speed, high-capacity trains.
除了在人工智能物联网和人工智能算法方面满足工业 4.0 的智能发展需求外，工业 5.0 引入了以人为本的理念，强调技术发展，优先考虑人类福祉、社会进步和环境可持续性。本节重点介绍轨道交通领域的新兴技术，包括人机交互的进步、数字物理空间的双向分析，以及对高速、大容量列车的改进。

Large language models (LLMs) for human-machine interaction
用于人机交互的大型语言模型 (LLMs)

As mentioned before, AI-based methods can automatically analyze data and provide accurate predictions by learning from historical data. Particularly, recent revolutionary advancements in large language models (LLMs) have good performance in dealing with natural language processing (NLP).¹⁶¹ Wang et al.¹⁶² proposed integrating LLMs with sensor networks and monitoring systems to analyze operational historical data and equipment status, thus generating corresponding alerts. LLMs can also be utilized to develop intelligent operator assistance systems in order to offer real-time, precise recommendations and decision support, thus facilitating human-machine collaboration advocated by Industry 5.0. Due to their characteristics, LLMs can create domain-specific knowledge bases and use instructions or prompts for fine-tuning. These operations both allow customized task handling in different scenarios.
如前所述，基于人工智能的方法可以自动分析数据，并通过学习历史数据提供准确的预测。特别是最近在大型语言模型（LLMs）方面取得的革命性进展，在处理自然语言处理（NLP）方面具有良好的性能。 ¹⁶¹ Wang 等人 ¹⁶² 提出将LLMs与传感器网络和监控系统集成，分析运行历史数据和设备状态，从而生成相应的警报。LLMs 还可用于开发智能操作员辅助系统，以提供实时、精确的建议和决策支持，从而促进工业 5.0 所倡导的人机协作。由于其特性，LLMs 可以创建特定领域的知识库，并使用指令或提示进行微调。这些操作都允许在不同场景中处理定制任务。

As the LLM algorithm becomes the frontier research, there is currently a scarcity of literature related to self-powered sensing or IoT studies. Nevertheless, LLM holds significant potentials in rail transit, particularly in predictive maintenance and health management (PHM) systems which includes real-time monitoring and assessment of railway equipment as well as forecasting potential failures.¹⁶³ In the previously discussed self-powered sensing and IoT framework, conditions such as abundant energy supply and intelligent sensor management have facilitated the widespread implementation of PHM in rail transit through other ML or DL algorithms. Consequently, it is believed that LLM can be well integrated into self-powered sensing systems in the future to achieve advanced PHM in rail transit.
随着LLM算法成为前沿研究领域，目前与自供电传感或物联网研究相关的文献还很少。然而，LLM在轨道交通领域具有巨大的潜力，尤其是在预测性维护和健康管理 (PHM) 系统中，其中包括对铁路设备的实时监控和评估，以及预测潜在故障。 ¹⁶³ 在前面讨论的自供电传感和物联网框架中，能源供应充足和智能传感器管理等条件促进了通过其他 ML 或 DL 算法在轨道交通中广泛实施 PHM。因此，我们相信未来LLM可以很好地集成到自供电传感系统中，从而在轨道交通中实现先进的 PHM。

Digital twin (DT) for the virtual
虚拟数字孪生（DT）

Digital twin (DT) technology gathers extensive data from physical spaces and conducts thorough analyses of entities. It aims to generate complete descriptions and display them in the information space, in order to enable precise remote management.¹⁶⁴^,¹⁶⁵ In rail transit, DT technology can develop dynamic, personalized digital simulations of physical trains, allowing for real-time applications such as predictive maintenance, adaptive optimization, and fault detection.¹⁶⁶^,¹⁶⁷^,¹⁶⁸ Liu et al.¹⁶⁹ established a DT framework to populate historical data of train operations for fault diagnosis. He also utilized virtual reality mapping within the DT framework to optimize the dynamics model of train bearings. Like most technologies developed under the AIoT framework, DT also integrates ML or DL algorithms to make data-driven intelligent decisions, such as pattern recognition and predictive analysis in virtual environments. Wu et al.¹⁷⁰ proposed a DT-based bogie fault diagnosis framework that integrates seven dimensions of data (from physical equipment to supplier networks and utilizes a multi-layer CNN data-driven model for fault diagnosis to address the challenge of real-time monitoring of bogies. Bosso et al.¹⁷¹ employed SVM algorithms for multibody simulations within the DT framework. By combining both techniques, modeling capabilities for train dynamics analysis can be enhanced.
数字孪生（DT）技术从物理空间收集大量数据，并对实体进行全面分析。其目的是生成完整的描述并将其显示在信息空间中，从而实现精确的远程管理。 ¹⁶⁴ ¹⁶⁵ 在轨道交通领域，DT 技术可以对物理列车进行动态、个性化的数字模拟，从而实现预测性维护、自适应优化和故障检测等实时应用。 ¹⁶⁶ ¹⁶⁷ ¹⁶⁸ Liu 等人 ¹⁶⁹ 建立了一个 DT 框架，用于填充列车运行的历史数据以进行故障诊断。他还在 DT 框架内利用虚拟现实映射来优化列车轴承的动力学模型。与 AIoT 框架下开发的大多数技术一样，DT 也集成了 ML 或 DL 算法，以做出数据驱动的智能决策，如虚拟环境中的模式识别和预测分析。Wu 等人 ¹⁷⁰ 提出了基于 DT 的转向架故障诊断框架，该框架集成了七个维度的数据（从物理设备到供应商网络），并利用多层 CNN 数据驱动模型进行故障诊断，以应对转向架实时监控的挑战。Bosso 等人 ¹⁷¹ 在 DT 框架内采用 SVM 算法进行多体模拟。通过结合这两种技术，可以增强列车动力学分析的建模能力。

Although the literature about DT technology with self-powered sensing systems is limited, numerous studies have focused on enhancing rail safety, maintenance, and efficiency through the DT framework. Wang et al.¹⁷² analyzed the necessity for DT technology in traction power supply systems and provided a comprehensive discussion on its application modes for energy conservation. The article highlights that DT can facilitate the self-evolution and updating of rail or track models. Therefore, it enables the collection of status data from various dimensions, thereby achieving parallel control over energy consumption and operations with high reliability and efficiency in rail transit.
虽然有关自供电传感系统 DT 技术的文献有限，但许多研究都侧重于通过 DT 框架提高铁路安全、维护和效率。Wang 等人 ¹⁷² 分析了 DT 技术在牵引供电系统中的必要性，并全面探讨了其在节能方面的应用模式。文章强调，DT 可以促进轨道或铁轨模型的自我进化和更新。因此，它可以从多个维度收集状态数据，从而在轨道交通中以高可靠性和高效率实现对能源消耗和运营的并行控制。

High-temperature superconducting (HTS) and magnetic levitation (maglev) for high-speed railways
用于高速铁路的高温超导（HTS）和磁悬浮（maglev）

As materials science develops, the increasingly mature high-temperature superconducting (HTS) technology has begun to be applied in rail transit scenarios, due to its excellent energy transmission characteristics.¹⁷³ Allais et al.¹⁷⁴ proposed the SuperRail project, which involves the development, manufacturing, installation, and long-term operation of HTS direct current cable systems for railway applications. It aims to enhance the power supply in the train station. Magnetic levitation (maglev) technology based on HTS materials can enable trains to maintain stable levitation without any control system or operational speed.¹⁷⁵ HTS maglev vehicle models do not require onboard traction power devices or wheel traction adhesion, making the vehicles relatively lightweight and demonstrating significant potential in high-speed rail transit. In Figure 13A, Fu et al.¹⁷⁶ discussed how the performance of vehicle-track coupled vibrations affects vehicle operational stability and provided reasonable recommendations to improve the safety of such high-speed trains. Furthermore, control and optimization algorithms are also applicable to trains supported by this technology. Li et al.¹⁷⁷ employed a fuzzy-PID algorithm to control the secondary suspension of HTS maglev vehicles, enhancing ride comfort and offering better insights for ergonomic development in high-speed railway applications. Figure 13B shows the practical achievements of HTS maglev technology so far.
随着材料科学的发展，日趋成熟的高温超导（HTS）技术因其卓越的能量传输特性开始应用于轨道交通领域。 ¹⁷³ Allais等人 ¹⁷⁴ 提出了 "超级铁路"（SuperRail）项目，该项目涉及铁路应用中高温超导直流电缆系统的开发、制造、安装和长期运行。该项目旨在增强火车站的电力供应。基于 HTS 材料的磁悬浮（maglev）技术可以使列车在没有任何控制系统或运行速度的情况下保持稳定的悬浮状态。 ¹⁷⁵ HTS磁悬浮列车模型不需要车载牵引动力装置或车轮牵引附着力，因此车辆相对较轻，在高速轨道交通领域展现出巨大潜力。在图 13A 中，Fu 等人 ¹⁷⁶ 讨论了车辆-轨道耦合振动的性能如何影响车辆的运行稳定性，并为提高此类高速列车的安全性提供了合理建议。此外，控制和优化算法也适用于该技术支持的列车。Li等人 ¹⁷⁷ 采用模糊PID算法控制HTS磁悬浮列车的二级悬挂，提高了乘坐舒适性，为高速铁路应用中的人机工程学发展提供了更好的启示。图 13B 显示了 HTS 磁悬浮技术迄今为止取得的实际成果。

Conclusion and challenges
结论与挑战

As the global railway network expands and develops, self-powered and self-sensing devices have become increasingly effective in various rail transit scenarios. This review offers a comprehensive review of the development characteristics of autonomous energy harvesting devices in the railway industry. It summarizes the current features and implementation methods of self-powered devices from multiple perspectives, including power generation methods, material selection, and application scenarios. Additionally, it analyzes these devices within the existing IoT framework, discussing the self-sensing capabilities enabled by their integration with IoT functionalities such as sensing, communication, and computation. Through extensive research discussions, it is evident that by using the network layer in the IoT architecture as a bridge, mechanical devices at the physical layer can be combined with various advanced algorithms at the application layer. All the examples showcased the potential and possibilities of self-powered and self-sensing devices in rail transit.
随着全球铁路网络的不断扩大和发展，自供电和自感应设备在各种轨道交通应用场景中变得越来越有效。本综述全面回顾了铁路行业自主能量采集设备的发展特点。它从发电方法、材料选择和应用场景等多个角度总结了当前自供电设备的特点和实现方法。此外，报告还分析了这些设备在现有物联网框架下的应用，讨论了这些设备与传感、通信和计算等物联网功能的集成所带来的自感应能力。通过广泛的研究讨论，我们可以明显看出，利用物联网架构中的网络层作为桥梁，物理层的机械设备可以与应用层的各种先进算法相结合。所有例子都展示了轨道交通中自供电和自传感设备的潜力和可能性。

The results indicate that continuous iteration and improvement of cutting-edge technologies (particularly DL algorithms) have provided comprehensive solutions to issues in rail transit. These methods enhance the utilization of sensor signals generated by the devices and offer mature AIoT solutions that combine IoT with rail transit. Therefore, the integration of self-powered and self-sensing devices with advanced technologies significantly addresses existing challenges in rail transit, especially for autonomous monitoring and the collection, processing, and transmission of complex signals in the plateau, remote mountainous and high-cold regions. However, the development of self-powered sensing technologies still faces several challenges, particularly in the integration of diverse technologies. Considering both spatial and economic costs, the coordination between self-powered devices and embedded systems (e.g., IoT communication modules or AI chips) has to be integrated into the overall design. As the researches integrate multiple technologies, the lack of coordination across different domains tends to be serious.
结果表明，尖端技术（尤其是 DL 算法）的不断迭代和改进为轨道交通问题提供了全面的解决方案。这些方法提高了设备产生的传感器信号的利用率，并提供了成熟的人工智能物联网解决方案，将物联网与轨道交通相结合。因此，将自供电和自传感设备与先进技术相结合，可以极大地解决轨道交通中的现有难题，尤其是在高原、偏远山区和高寒地区的自主监控以及复杂信号的采集、处理和传输方面。然而，自供电传感技术的发展仍面临一些挑战，特别是在多种技术的集成方面。考虑到空间成本和经济成本，自供电设备与嵌入式系统（如物联网通信模块或人工智能芯片）之间的协调必须融入整体设计中。随着多种技术的整合研究，不同领域之间缺乏协调的问题趋于严重。

Consequently, we have identified several key points and explored some emerging research directions, ultimately providing supplementary solutions and broader perspectives for the intelligent development of rail transit, shown in Figure 14.
因此，我们确定了几个关键点，并探索了一些新兴的研究方向，最终为轨道交通的智能化发展提供了补充解决方案和更广阔的视角，如图 14 所示。

Multifunctional deployment integration
多功能部署集成

Current self-powered technologies have significantly improved power generation efficiency and expanded power generation methods. Those devices that solely function as power sources are insufficient for adapting to changing environments and lack practical value. Although this review mainly focuses on scenarios involving self-powered applications, the number of related studies decreases as functionalities increase. Therefore, there is a growing emphasis on integrating multiple functions into a single self-powered device, including self-sensing, device optimization control, data communication, and algorithm deployment. Notably, advanced self-powered devices can integrate IoT and AI functionalities, such as those under the TinyML framework, via MEMS technology without compromising power efficiency.¹⁷⁸ In the environment of rail transit, the development direction of self-powered technology will continue to integrate and deploy multiple functions,¹⁷⁹ so as to assimilate and adapt to more advanced technologies.
目前的自供电技术大大提高了发电效率，并扩展了发电方式。那些仅作为电源的设备不足以适应不断变化的环境，缺乏实用价值。虽然本综述主要关注涉及自供电应用的场景，但随着功能的增加，相关研究的数量也在减少。因此，人们越来越重视将多种功能集成到单个自供电设备中，包括自感应、设备优化控制、数据通信和算法部署。值得注意的是，先进的自供电设备可通过 MEMS 技术集成物联网和人工智能功能，例如 TinyML 框架下的功能，而不会降低能效。 ¹⁷⁸ 在轨道交通环境中，自供电技术的发展方向将继续集成和部署多种功能， ¹⁷⁹ 以便吸收和适应更先进的技术。

Customization and improvement of technologies and algorithms
定制和改进技术与算法

As the railway environment becomes increasingly complex and variable, the energy and data sources collected by self-powered and self-sensing devices also become more diverse and heterogeneous. Single method in research have shown limitations, such as solar or wind energy collectors failing to function in adverse weather conditions. Specific hybrid energy harvesting methods can be implemented to address other effective energy sources in the environment.¹⁸⁰ It ensures that at least one usable resource is available for normal monitoring and maintenance of rail transit. Specific algorithm improvements are needed for issues such as the varying quality and incompleteness of collected information. Improvements include introducing noise and bias, lightweight models, and multi-modal information fusion.¹⁸¹ Lightweight models can increase the feasibility of deployment on embedded edge devices. At the same time, multi-modal information fusion methods can combine rich signals collected from the environment, enhancing the robustness of rail monitoring and vehicle status monitoring tasks.
随着铁路环境变得越来越复杂多变，自供电和自传感设备所收集的能源和数据源也变得越来越多样化和异质化。研究中的单一方法已显示出局限性，例如太阳能或风能收集器在恶劣天气条件下无法发挥作用。可以采用特定的混合能源采集方法来解决环境中的其他有效能源。 ¹⁸⁰ 它确保至少有一种可用资源可用于轨道交通的正常监控和维护。针对收集到的信息质量参差不齐和不完整等问题，需要对具体算法进行改进。改进措施包括引入噪声和偏差、轻量级模型和多模式信息融合。 ¹⁸¹ 轻量级模型可以提高在嵌入式边缘设备上部署的可行性。同时，多模式信息融合方法可以将从环境中收集到的丰富信号结合起来，增强轨道监控和车辆状态监控任务的鲁棒性。

Cross-disciplinary 跨学科

Some studies have already integrated rail self-powered and self-sensing technology with DL algorithms. Other technologies like wearable design¹⁸² and materials science¹⁸³ indicate that interdisciplinary applications remain a prevailing trend in technology development. Numerous studies have demonstrated that specific problems within a single field can be too complex and demanding to solve effectively. However, current research efforts can receive direct assistance by combining tools from other fields. Both algorithms aiding in data processing and more theoretical approaches to material structure design can facilitate better optimization of the installed devices in the rail environment. Furthermore, advanced analytical methods from other fields can enhance research efficiency through approaches similar to transfer learning.
一些研究已经将轨道自供电和自传感技术与 DL 算法结合起来。可穿戴设计 ¹⁸² 和材料科学 ¹⁸³ 等其他技术表明，跨学科应用仍然是技术发展的主流趋势。大量研究表明，单一领域内的具体问题可能过于复杂，要求过高，难以有效解决。然而，结合其他领域的工具可以直接帮助当前的研究工作。无论是辅助数据处理的算法，还是材料结构设计的理论方法，都有助于更好地优化轨道环境中安装的设备。此外，其他领域的先进分析方法也可以通过类似于迁移学习的方法提高研究效率。

Acknowledgments 致谢

This work was supported by the National Natural Foundation of China under Grants No. 51975490 and by the Science and Technology Projects of Sichuan under Grants Nos. 23QYCX0280 and 2022NSFSC0461; and by the Science and Technology Projects of Yibin under Grant No.2021ZYCG017, 2023SJXQYBKJJH005, No. SWJTU2021020001 and SWJTU2021020002; and by the Science and Technology Projects of Chengdu under Grant No. 2021YF0800138GX.
本研究得到国家自然科学基金（51975490）、四川省科技计划项目（23QYCX0280、2022NSFSC0461）、宜宾市科技计划项目（2021ZYCG017、2023SJXQYBKJJH005、SWJTU2021020001、SWJTU2021020002ZYCG017、2023SJXQYBKJJH005、编号 SWJTU2021020001 和 SWJTU2021020002；以及成都市科技项目，资助号 2021YF0800138GX。

Author contributions 作者供稿

H.T.: Methodology, Software, Writing - Original Draft. L.K.: Investigation, Resource, Writing – Review and Editing. Z.F.: Investigation, Resource, Formal analysis. Z.Z.: Supervision, Project administration, Funding acquisition. J.Z.: Formal analysis, Writing – Review and Editing. H.C.: Investigation, Resources. J.S.: Resources. X.Z.: Resources.
H.T.：方法、软件、写作--原稿。L.K.：调查、资源、写作--审阅和编辑。Z.F.：调查、资源、形式分析。Z.Z.：监督、项目管理、获取资金。J.Z：正式分析、写作--审查和编辑。H.C.：调查、资源。J.S.：资源。X.Z：资源

Declaration of interests 利益声明

The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this review.
作者声明，他们没有可能影响本综述所报告工作的已知经济利益或个人关系。

References

1
B. Jiang, C. Tian, J. Deng, Z. Zhu
China’s railway train speed, density and weight in developing
J. Hous. Built Environ., 1 (2022), pp. 131-147, 10.1108/RS-04-2022-0009
View at publisherGoogle Scholar
2
D.F. Dominković, I. Bačeković, A.S. Pedersen, G. Krajačić
The future of transportation in sustainable energy systems: Opportunities and barriers in a clean energy transition
Renew. Sustain. Energy Rev., 82 (2018), pp. 1823-1838, 10.1016/j.rser.2017.06.117
View PDF View article View in Scopus Google Scholar
3
N. Bosso, M. Magelli, N. Zampieri
Application of low-power energy harvesting solutions in the railway field: a review
Veh. Syst. Dyn., 59 (2021), pp. 841-871, 10.1080/00423114.2020.1726973
View in Scopus Google Scholar
4
N. Xu, J. Han, Y. Xiong, Z.L. Wang, Q. Sun
TENG Applications in Transportation and Surrounding Emergency Management
Adv. Sustain. Syst., 6 (2022), 10.1002/adsu.202200267
Google Scholar
5
J. Zuo, L. Dong, F. Yang, Z. Guo, T. Wang, L. Zuo
Energy harvesting solutions for railway transportation: A comprehensive review
Renew. Energy, 202 (2023), pp. 56-87, 10.1016/j.renene.2022.11.008
View PDF View article View in Scopus Google Scholar
6
R. Du, J. Xiao, S. Chang, L. Zhao, K. Wei, W. Zhang, H. Zou
Mechanical energy harvesting in traffic environment and its application in smart transportation
J. Phys. D Appl. Phys., 56 (2023), Article 373002, 10.1088/1361-6463/acdadb
View in Scopus Google Scholar
7
Y. Wang, S. Li, P. Wang, M. Gao, H. Ouyang, Q. He, P. Wang
A multifunctional electromagnetic device for vibration energy harvesting and rail corrugation sensing
Smart Mater. Struct., 30 (2021), Article 125012, 10.1088/1361-665X/ac31c5
View in Scopus Google Scholar
8
F. Ksica, O. Rubes, J. Kovar, J. Chalupa, Z. Hadas
Smart Sensing System for Railway Monitoring
2022 20th International Conference on Mechatronics - Mechatronika (ME), IEEE (2022), pp. 1-6, 10.1109/ME54704.2022.9983339
Google Scholar
9
H. Salehi, R. Burgueño, S. Chakrabartty, N. Lajnef, A.H. Alavi
A comprehensive review of self-powered sensors in civil infrastructure: State-of-the-art and future research trends
Eng. Struct., 234 (2021), Article 111963, 10.1016/j.engstruct.2021.111963
View PDF View article View in Scopus Google Scholar
10
P. Singh, Z. Elmi, V. Krishna Meriga, J. Pasha, M.A. Dulebenets
Internet of Things for sustainable railway transportation: Past, present, and future
Cleaner Logistics and Supply Chain, 4 (2022), Article 100065, 10.1016/j.clscn.2022.100065
View PDF View article View in Scopus Google Scholar
11
X. Zhao, H. Askari, J. Chen
Nanogenerators for smart cities in the era of 5G and Internet of Things
Joule, 5 (2021), pp. 1391-1431, 10.1016/j.joule.2021.03.013
View PDF View article View in Scopus Google Scholar
12
D. Yang, E. Cui, H. Wang, H. Zhang
EH-Edge--An Energy Harvesting-Driven Edge IoT Platform for Online Failure Prediction of Rail Transit Vehicles: A case study of a cloud, edge, and end device collaborative computing paradigm
IEEE Veh. Technol. Mag., 16 (2021), pp. 95-103, 10.1109/MVT.2021.3053193
View in Scopus Google Scholar
13
F. Zanelli, M. Mauri, F. Castelli-Dezza, E. Sabbioni, D. Tarsitano, N. Debattisti
Energy Autonomous Wireless Sensor Nodes for Freight Train Braking Systems Monitoring
Sensors, 22 (2022), p. 1876, 10.3390/s22051876
View in Scopus Google Scholar
14
R. Dirnfeld, F. Flammini, S. Marrone, R. Nardone, V. Vittorini
Low-Power Wide-Area Networks in Intelligent Transportation: Review and Opportunities for Smart-Railways
2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) (IEEE) (2020), pp. 1-7, 10.1109/ITSC45102.2020.9294535
Google Scholar
15
J. Zhang, Y. Yu, L. Zhang, J. Chen, X. Wang, X. Wang
Dig information of nanogenerators by machine learning
Nano Energy, 114 (2023), Article 108656, 10.1016/j.nanoen.2023.108656
View PDF View article View in Scopus Google Scholar
16
R. Tang, L. De Donato, N. Besinović, F. Flammini, R.M.P. Goverde, Z. Lin, R. Liu, T. Tang, V. Vittorini, Z. Wang
A literature review of Artificial Intelligence applications in railway systems
Transport. Res. C Emerg. Technol., 140 (2022), Article 103679, 10.1016/j.trc.2022.103679
View PDF View article View in Scopus Google Scholar
17
X. Gibert, V.M. Patel, R. Chellappa
Deep Multitask Learning for Railway Track Inspection
IEEE Trans. Intell. Transport. Syst., 18 (2017), pp. 153-164, 10.1109/TITS.2016.2568758
View in Scopus Google Scholar
18
M. Akhtar, S. Moridpour
A Review of Traffic Congestion Prediction Using Artificial Intelligence
J. Adv. Transport., 2021 (2021), pp. 1-18, 10.1155/2021/8878011
Google Scholar
19
A. Boukerche, J. Wang
A performance modeling and analysis of a novel vehicular traffic flow prediction system using a hybrid machine learning-based model
Ad Hoc Netw., 106 (2020), Article 102224, 10.1016/j.adhoc.2020.102224
View PDF View article View in Scopus Google Scholar
20
C. Wen, P. Huang, Z. Li, J. Lessan, L. Fu, C. Jiang, X. Xu
Train Dispatching Management With Data- Driven Approaches: A Comprehensive Review and Appraisal
IEEE Access, 7 (2019), pp. 114547-114571, 10.1109/ACCESS.2019.2935106
View in Scopus Google Scholar
21
A. Alagumalai, W. Shou, O. Mahian, M. Aghbashlo, M. Tabatabaei, S. Wongwises, Y. Liu, J. Zhan, A. Torralba, J. Chen, et al.
Self-powered sensing systems with learning capability
J. Inflamm. Res., 15 (2022), pp. 3083-3094, 10.1016/j.joule.2022.06.001
Google Scholar
22
N. Besinovic, L. De Donato, F. Flammini, R.M.P. Goverde, Z. Lin, R. Liu, S. Marrone, R. Nardone, T. Tang, V. Vittorini
Artificial Intelligence in Railway Transport: Taxonomy, Regulations, and Applications
IEEE Trans. Intell. Transport. Syst., 23 (2022), pp. 14011-14024, 10.1109/TITS.2021.3131637
View in Scopus Google Scholar
23
N. Zhao, H. Zhang, X. Yang, J. Yan, F. You
Emerging information and communication technologies for smart energy systems and renewable transition
Adv. Appl. Energy, 9 (2023), Article 100125, 10.1016/j.adapen.2023.100125
View PDF View article View in Scopus Google Scholar
24
C. Laiton-Bonadiez, J.W. Branch-Bedoya, J. Zapata-Cortes, E. Paipa-Sanabria, M. Arango-Serna
Industry 4.0 Technologies Applied to the Rail Transportation Industry: A Systematic Review
Sensors, 22 (2022), p. 2491, 10.3390/s22072491
View in Scopus Google Scholar
25
G. Li, S.W. Or, K.W. Chan
Intelligent Energy-Efficient Train Trajectory Optimization Approach Based on Supervised Reinforcement Learning for Urban Rail Transits
IEEE Access, 11 (2023), pp. 31508-31521, 10.1109/ACCESS.2023.3261900
View in Scopus Google Scholar
26
T. Zhang, T. Yang, M. Zhang, C.R. Bowen, Y. Yang
Recent Progress in Hybridized Nanogenerators for Energy Scavenging
iScience, 23 (2020), Article 101689, 10.1016/j.isci.2020.101689
View PDF View article View in Scopus Google Scholar
27
Z. Wang, W. Wang, F. Gu, C. Wang, Q. Zhang, G. Feng, A.D. Ball
On-rotor electromagnetic energy harvester for powering a wireless condition monitoring system on bogie frames
Energy Convers. Manag., 243 (2021), Article 114413, 10.1016/j.enconman.2021.114413
View PDF View article View in Scopus Google Scholar
28
Y. Du, Q. Tang, W. He, W. Liu, Z. Wang, H. Wu, G. Li, H. Guo, Z. Li, Y. Peng, C. Hu
Harvesting ambient mechanical energy by multiple mode triboelectric nanogenerator with charge excitation for self-powered freight train monitoring
Nano Energy, 90 (2021), Article 106543, 10.1016/j.nanoen.2021.106543
View PDF View article View in Scopus Google Scholar
29
Z. Wang, W. Wang, L. Tang, R. Tian, C. Wang, Q. Zhang, C. Liu, F. Gu, A.D. Ball
A piezoelectric energy harvester for freight train condition monitoring system with the hybrid nonlinear mechanism
Mech. Syst. Signal Process., 180 (2022), Article 109403, 10.1016/j.ymssp.2022.109403
View PDF View article View in Scopus Google Scholar
30
G. Shan, D. Wang, Z.J. Chew, M. Zhu
A high-power, robust piezoelectric energy harvester for wireless sensor networks in railway applications
Sens Actuators A Phys, 360 (2023), Article 114525, 10.1016/j.sna.2023.114525
View PDF View article View in Scopus Google Scholar
31
M. Gao, J. Cong, J. Xiao, Q. He, S. Li, Y. Wang, Y. Yao, R. Chen, P. Wang
Dynamic modeling and experimental investigation of self-powered sensor nodes for freight rail transport
Appl. Energy, 257 (2020), Article 113969, 10.1016/j.apenergy.2019.113969
View PDF View article View in Scopus Google Scholar
32
Y. Pan, L. Zuo, M. Ahmadian
A half-wave electromagnetic energy-harvesting tie towards safe and intelligent rail transportation
Appl. Energy, 313 (2022), Article 118844, 10.1016/j.apenergy.2022.118844
View PDF View article View in Scopus Google Scholar
33
W. Salman, C. Fan, H. Pan, Z. Zhang, X. Wu, M. Abdelrahman, A.M. Tairab, A. Ali
A dual-kinetic energy harvester operating on the track and wheel of rail deceleration system for self-powered sensors
Smart Mater. Struct., 32 (2023), Article 125023, 10.1088/1361-665X/ad0b1a
View in Scopus Google Scholar
34
D. Hao, T. Zhang, L. Guo, Y. Feng, Z. Zhang, Y. Yuan
A High-Efficiency, Portable Solar Energy-Harvesting System Based on a Foldable-Wings Mechanism for Self-Powered Applications in Railways
Energ. Tech., 9 (2021), Article 2000794, 10.1002/ente.202000794
View in Scopus Google Scholar
35
H. Foltz, C. Tarawneh, M. Amaro, S. Thomas, D. Capitanachi Avila
Thermoelectric energy harvesting for wireless onboard rail condition monitoring
Int. J. Real. Ther., 12 (2023), pp. 514-531, 10.1080/23248378.2023.2201247
Google Scholar
36
Y. Wang, X. Zhu, T. Zhang, S. Bano, H. Pan, L. Qi, Z. Zhang, Y. Yuan
A renewable low-frequency acoustic energy harvesting noise barrier for high-speed railways using a Helmholtz resonator and a PVDF film
Appl. Energy, 230 (2018), pp. 52-61, 10.1016/j.apenergy.2018.08.080
View PDF View article Google Scholar
37
P. Lopez Diez, I. Gabilondo, E. Alarcon, F. Moll
Mechanical Energy Harvesting Taxonomy for Industrial Environments: Application to the Railway Industry
IEEE Trans. Intell. Transport. Syst., 21 (2020), pp. 2696-2706, 10.1109/TITS.2019.2924987
View in Scopus Google Scholar
38
G. Jing, M. Siahkouhi, J. Riley Edwards, M.S. Dersch, N.A. Hoult
Smart railway sleepers - a review of recent developments, challenges, and future prospects
Construct. Build. Mater., 271 (2021), Article 121533, 10.1016/j.conbuildmat.2020.121533
View PDF View article View in Scopus Google Scholar
39
S. Zheng, Z. Wu, Y. Xu, Z. Wei
Intrusion Detection of Foreign Objects in Overhead Power System for Preventive Maintenance in High-Speed Railway Catenary Inspection
IEEE Trans. Instrum. Meas., 71 (2022), pp. 1-12, 10.1109/TIM.2022.3189642
Google Scholar
40
B. Xiong, H. Wang, L. Wang, Z. Zhang, Y. Pan, T. Liu, M. Tang, G. Liu, Y. Hu
Sustainable wind barrier: Self-powered system for high-speed railway safety monitoring
Sustain. Mater. Technol., 39 (2024), Article e00848, 10.1016/j.susmat.2024.e00848
View PDF View article View in Scopus Google Scholar
41
C. Jia, H. Li, X. Zhou, Z. Fang, X. Wu, Z. Zhang
A renewable energy harvesting wind barrier based on coaxial contrarotation for self-powered applications on railways
Energy, 258 (2022), Article 124842, 10.1016/j.energy.2022.124842
Google Scholar
42
H. Pan, H. Li, T. Zhang, A.A. Laghari, Z. Zhang, Y. Yuan, B. Qian
A portable renewable wind energy harvesting system integrated S-rotor and H-rotor for self-powered applications in high-speed railway tunnels
Energy Convers. Manag., 196 (2019), pp. 56-68, 10.1016/j.enconman.2019.05.115
View PDF View article View in Scopus Google Scholar
43
P. Zheng, L. Qi, M. Sun, D. Luo, Z. Zhang
A novel wind energy harvesting system with hybrid mechanism for self-powered applications in subway tunnels
Energy, 227 (2021), Article 120446, 10.1016/j.energy.2021.120446
View PDF View article View in Scopus Google Scholar
44
W. Guo, Y. Long, Z. Bai, X. Wang, H. Liu, Z. Guo, S. Tan, H. Guo, Y. Wang, Y. Miao
Variable stiffness triboelectric nano-generator to harvest high-speed railway bridge’s vibration energy
Energy Convers. Manag., 268 (2022), Article 115969, 10.1016/j.enconman.2022.115969
View PDF View article View in Scopus Google Scholar
45
K. Huang, Y. Zhou, Z. Zhang, H. Zhang, C. Lü, J. Luo, L. Shen
A real-time quantitative acceleration monitoring method based on triboelectric nanogenerator for bridge cable vibration
Nano Energy, 118 (2023), Article 108960, 10.1016/j.nanoen.2023.108960
View PDF View article View in Scopus Google Scholar
46
O. Jo, Y.-K. Kim, J. Kim
Internet of Things for Smart Railway: Feasibility and Applications
IEEE Internet Things J., 5 (2018), pp. 482-490, 10.1109/JIOT.2017.2749401
View in Scopus Google Scholar
47
L. Jin, W. Deng, Y. Su, Z. Xu, H. Meng, B. Wang, H. Zhang, B. Zhang, L. Zhang, X. Xiao, et al.
Self-powered wireless smart sensor based on maglev porous nanogenerator for train monitoring system
Nano Energy, 38 (2017), pp. 185-192, 10.1016/j.nanoen.2017.05.018
View PDF View article View in Scopus Google Scholar
48
H. Zhang, C. Yang, Y. Yu, Y. Zhou, L. Quan, S. Dong, J. Luo
Origami-tessellation-based triboelectric nanogenerator for energy harvesting with application in road pavement
Nano Energy, 78 (2020), Article 105177, 10.1016/j.nanoen.2020.105177
View PDF View article View in Scopus Google Scholar
49
Y. Sun, Y. Yan, S. Tian, G. Liu, F. Wu, P. Wang, M. Gao
Wireless sensing in high-speed railway turnouts with battery-free materials and devices
iScience, 27 (2024), Article 108663, 10.1016/j.isci.2023.108663
View PDF View article View in Scopus Google Scholar
50
Z. Lin, C. Sun, W. Liu, E. Fan, G. Zhang, X. Tan, Z. Shen, J. Qiu, J. Yang
A self-powered and high-frequency vibration sensor with layer-powder-layer structure for structural health monitoring
Nano Energy, 90 (2021), Article 106366, 10.1016/j.nanoen.2021.106366
View PDF View article View in Scopus Google Scholar
51
Y. Meng, J. Yang, S. Liu, W. Xu, G. Chen, Z. Niu, M. Wang, T. Deng, Y. Qin, M. Han, X. Li
Nano-fiber based self-powered flexible vibration sensor for rail fasteners tightness safety detection
Nano Energy, 102 (2022), Article 107667, 10.1016/j.nanoen.2022.107667
View PDF View article View in Scopus Google Scholar
52
J. Qian, D.-S. Kim, D.-W. Lee
On-vehicle triboelectric nanogenerator enabled self-powered sensor for tire pressure monitoring
Nano Energy, 49 (2018), pp. 126-136, 10.1016/j.nanoen.2018.04.022
View PDF View article View in Scopus Google Scholar
53
Y. Feng, X. Huang, S. Liu, W. Guo, Y. Li, H. Wu
A self-powered smart safety belt enabled by triboelectric nanogenerators for driving status monitoring
Nano Energy, 62 (2019), pp. 197-204, 10.1016/j.nanoen.2019.05.043
View PDF View article View in Scopus Google Scholar
54
J.Y. Cho, K.-B. Kim, W.S. Hwang, C.H. Yang, J.H. Ahn, S.D. Hong, D.H. Jeon, G.J. Song, C.H. Ryu, S.B. Woo, et al.
A multifunctional road-compatible piezoelectric energy harvester for autonomous driver-assist LED indicators with a self-monitoring system
Appl. Energy, 242 (2019), pp. 294-301, 10.1016/j.apenergy.2019.03.075
View PDF View article View in Scopus Google Scholar
55
V.N. Thakur, J.I. Han
Triboelectric nanogenerator for smart traffic monitoring and safety
J. Ind. Eng. Chem., 124 (2023), pp. 89-101, 10.1016/j.jiec.2023.04.028
View PDF View article View in Scopus Google Scholar
56
X. Cao, Y. Xiong, J. Sun, X. Zhu, Q. Sun, Z.L. Wang
Piezoelectric Nanogenerators Derived Self-Powered Sensors for Multifunctional Applications and Artificial Intelligence
Adv. Funct. Mater., 31 (2021), 10.1002/adfm.202102983
Google Scholar
57
L. Farhan, O. Kaiwartya, L. Alzubaidi, W. Gheth, E. Dimla, R. Kharel
Toward Interference Aware IoT Framework: Energy and Geo-Location-Based-Modeling
IEEE Access, 7 (2019), pp. 56617-56630, 10.1109/ACCESS.2019.2913899
View in Scopus Google Scholar
58
F.K. Shaikh, S. Zeadally
Energy harvesting in wireless sensor networks: A comprehensive review
Renew. Sustain. Energy Rev., 55 (2016), pp. 1041-1054, 10.1016/j.rser.2015.11.010
View PDF View article View in Scopus Google Scholar
59
W. Balid, H.H. Refai
On the development of self-powered iot sensor for real-time traffic monitoring in smart cities
2017 IEEE SENSORS, IEEE (2017), pp. 1-3, 10.1109/ICSENS.2017.8234157
View in Scopus Google Scholar
60
H. Askari, E. Hashemi, A. Khajepour, M.B. Khamesee, Z.L. Wang
Tire Condition Monitoring and Intelligent Tires Using Nanogenerators Based on Piezoelectric, Electromagnetic, and Triboelectric Effects
Adv. Mater. Technol., 4 (2019), 10.1002/admt.201800105
Google Scholar
61
L. Liu, X. Guo, W. Liu, C. Lee
Recent Progress in the Energy Harvesting Technology—From Self-Powered Sensors to Self-Sustained IoT, and New Applications
Nanomaterials, 11 (2021), p. 2975, 10.3390/nano11112975
View in Scopus Google Scholar
62
V.J. Hodge, S. O’Keefe, M. Weeks, A. Moulds
Wireless Sensor Networks for Condition Monitoring in the Railway Industry: A Survey
IEEE Trans. Intell. Transport. Syst., 16 (2015), pp. 1088-1106, 10.1109/TITS.2014.2366512
View in Scopus Google Scholar
63
H. Alawad, S. Kaewunruen
Wireless Sensor Networks: Toward Smarter Railway Stations
Infrastructure, 3 (2018), p. 24, 10.3390/infrastructures3030024
View in Scopus Google Scholar
64
M.I.M. Ismail, R.A. Dziyauddin, R. Ahmad, N. Ahmad, N.A. Ahmad, A.M.A. Hamid
A Review of Energy Harvesting in Localization for Wireless Sensor Node Tracking
IEEE Access, 9 (2021), pp. 60108-60122, 10.1109/ACCESS.2021.3072061
View in Scopus Google Scholar
65
D. Milne, L. Le Pen, G. Watson, D. Thompson, W. Powrie, M. Hayward, S. Morley
Proving MEMS Technologies for Smarter Railway Infrastructure
Procedia Eng., 143 (2016), pp. 1077-1084, 10.1016/j.proeng.2016.06.222
View PDF View article View in Scopus Google Scholar
66
B. Dziadak, M. Kucharek, J. Starzyński
Powering the WSN Node for Monitoring Rail Car Parameters, Using a Piezoelectric Energy Harvester
Energies, 15 (2022), p. 1641, 10.3390/en15051641
View in Scopus Google Scholar
67
M. Huang, M. Zhu, X. Feng, Z. Zhang, T. Tang, X. Guo, T. Chen, H. Liu, L. Sun, C. Lee
Intelligent Cubic-Designed Piezoelectric Node (iCUPE) with Simultaneous Sensing and Energy Harvesting Ability toward Self-Sustained Artificial Intelligence of Things (AIoT)
ACS Nano, 17 (2023), pp. 6435-6451, 10.1021/acsnano.2c11366
View in Scopus Google Scholar
68
Y. Gong, S. Wang, Z. Xie, T. Zhang, W. Chen, X. Lu, Q. Zeng, Y. Gao, W. Huang
Self-Powered Wireless Sensor Node for Smart Railway Axle Box Bearing via a Variable Reluctance Energy Harvesting System
IEEE Trans. Instrum. Meas., 70 (2021), pp. 1-11, 10.1109/TIM.2021.3076857
View in Scopus Google Scholar
69
J. Lu, M. Gao, Y. Wang, P. Wang
Health monitoring of urban rail corrugation by wireless rechargeable sensor nodes
Struct. Health Monit., 18 (2019), pp. 838-852, 10.1177/1475921718782395
View in Scopus Google Scholar
70
M.V. Lopes, J.J. Eckert, T.S. Martins, A.A. Santos
Optimizing strain energy extraction from multi-beam piezoelectric devices for heavy haul freight cars
J. Braz. Soc. Mech. Sci. Eng., 42 (2020), p. 59, 10.1007/s40430-019-2150-8
Google Scholar
71
Y. Wu, N. Yang
Study of rail damage diagnosis and localization method based on intelligent wireless acoustic sensor network
Measurement, 217 (2023), Article 113032, 10.1016/j.measurement.2023.113032
View PDF View article View in Scopus Google Scholar
72
L. Wang, T. He, Z. Zhang, L. Zhao, C. Lee, G. Luo, Q. Mao, P. Yang, Q. Lin, X. Li, et al.
Self-sustained autonomous wireless sensing based on a hybridized TENG and PEG vibration mechanism
Nano Energy, 80 (2021), Article 105555, 10.1016/j.nanoen.2020.105555
View PDF View article View in Scopus Google Scholar
73
S. Cii, G. Tomasini, M.L. Bacci, D. Tarsitano
Solar Wireless Sensor Nodes for Condition Monitoring of Freight Trains
IEEE Trans. Intell. Transport. Syst., 23 (2022), pp. 3995-4007, 10.1109/TITS.2020.3038319
View in Scopus Google Scholar
74
Z. Kljaic, M. Cipek, T.J. Mlinaric, D. Pavkovic, D. Zorc
Utilization of Track Condition Information from Remote Wireless Sensor Network in Railways-A Mountainous Rail Track Case Study
2019 27th Telecommunications Forum (TELFOR), IEEE (2019), pp. 1-4, 10.1109/TELFOR48224.2019.8971066
Google Scholar
75
P. Li, Z. Long, Z. Yang
RF Energy Harvesting for Batteryless and Maintenance-Free Condition Monitoring of Railway Tracks
IEEE Internet Things J., 8 (2021), pp. 3512-3523, 10.1109/JIOT.2020.3023475
View in Scopus Google Scholar
76
Y. Bi, D. Feng, S. Liu, L. Jia, Y. Meng, W. Xu, J. Yang, S. Liu, B. Wang, Y. Qin, X. Li
Solid ion channels gel battery driven by triboelectric effect and its integrated self-powered foreign matter intrusion detecting system
Nano Energy, 83 (2021), Article 105791, 10.1016/j.nanoen.2021.105791
View PDF View article View in Scopus Google Scholar
77
E.D. Spyrou, T. Tsenis, V. Kappatos
Experimental Setup of integrated Wireless Accelerometer Network with LTE Gateway for a Rail Car Body Health Monitoring Application
2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), IEEE (2021), pp. 1-6, 10.1109/ICECCME52200.2021.9591140
Google Scholar
78
R. He, B. Ai, G. Wang, K. Guan, Z. Zhong, A.F. Molisch, C. Briso-Rodriguez, C.P. Oestges
High-Speed Railway Communications: From GSM-R to LTE-R
IEEE Veh. Technol. Mag., 11 (2016), pp. 49-58, 10.1109/MVT.2016.2564446
View in Scopus Google Scholar
79
R. He, B. Ai, Z. Zhong, M. Yang, R. Chen, J. Ding, Z. Ma, G. Sun, C. Liu
5G for Railways: Next Generation Railway Dedicated Communications
IEEE Commun. Mag., 60 (2022), pp. 130-136, 10.1109/MCOM.005.2200328
View in Scopus Google Scholar
80
J. Sramota, A. Skavhaug
RailCheck: A WSN-Based System for Condition Monitoring of Railway Infrastructure
2018 21st Euromicro Conference on Digital System Design (DSD), IEEE (2018), pp. 347-351, 10.1109/DSD.2018.00067
View in Scopus Google Scholar
81
Q. Quevy, G. Cornetta, A. Touhafi
A New Method for Acoustic Priority Vehicle Detection Based on a Self-Powering Triboelectric Acoustic Sensor Suitable for Low-Power Wireless Sensor Networks
Sensors, 21 (2020), p. 158, 10.3390/s21010158
Google Scholar
82
Q. Gao, B. Yang, J. Huo, J. Han
Embedded real-time and in-situ fatigue life monitoring sensor with load types identification
Sens Actuators A Phys, 347 (2022), Article 113945, 10.1016/j.sna.2022.113945
View PDF View article View in Scopus Google Scholar
83
N. Magaia, P. Gomes, L. Silva, B. Sousa, C.X. Mavromoustakis, G. Mastorakis
Development of Mobile IoT Solutions: Approaches, Architectures, and Methodologies
IEEE Internet Things J., 8 (2021), pp. 16452-16472, 10.1109/JIOT.2020.3046441
View in Scopus Google Scholar
84
T. Kamakshi, K. Pritika
Smart Railway Infrastructure: Fusion of IoT, RFID and VANET for Enhanced Safety and Operational Efficiency
2023 Second International Conference on Advances in Computational Intelligence and Communication (ICACIC), IEEE (2023), pp. 1-6, 10.1109/ICACIC59454.2023.10435249
Google Scholar
85
P. Fraga-Lamas, T.M. Fernández-Caramés, L. Castedo
Towards the Internet of Smart Trains: A Review on Industrial IoT-Connected Railways
Sensors, 17 (2017), p. 1457, 10.3390/s17061457
View in Scopus Google Scholar
86
Z. Wang, H. Du, W. Wang, Q. Zhang, F. Gu, A.D. Ball, C. Liu, X. Jiao, H. Qiu, D. Shi
A high performance contra-rotating energy harvester and its wireless sensing application toward green and maintain free vehicle monitoring
Appl. Energy, 356 (2024), Article 122370, 10.1016/j.apenergy.2023.122370
View PDF View article View in Scopus Google Scholar
87
X. Zhao, G. Wei, X. Li, Y. Qin, D. Xu, W. Tang, H. Yin, X. Wei, L. Jia
Self-powered triboelectric nano vibration accelerometer based wireless sensor system for railway state health monitoring
Nano Energy, 34 (2017), pp. 549-555, 10.1016/j.nanoen.2017.02.036
View PDF View article View in Scopus Google Scholar
88
M. Magno, F. Tombari, D. Brunelli, L. Di Stefano, L. Benini
Multimodal Video Analysis on Self-Powered Resource-Limited Wireless Smart Camera
IEEE J. Emerg. Sel. Top. Circuits Syst., 3 (2013), pp. 223-235, 10.1109/JETCAS.2013.2256833
View in Scopus Google Scholar
89
W. He, W. Shi, J. Le, H. Li, R. Ma
Geophone-Based Energy Harvesting Approach for Railway Wagon Monitoring Sensor With High Reliability and Simple Structure
IEEE Access, 8 (2020), pp. 35882-35891, 10.1109/ACCESS.2020.2968089
View in Scopus Google Scholar
90
W. Hwang, K.-B. Kim, J.Y. Cho, C.H. Yang, J.H. Kim, G.J. Song, Y. Song, D.H. Jeon, J.H. Ahn, S. Do Hong, et al.
Watts-level road-compatible piezoelectric energy harvester for a self-powered temperature monitoring system on an actual roadway
Appl. Energy, 243 (2019), pp. 313-320, 10.1016/j.apenergy.2019.03.122
View PDF View article View in Scopus Google Scholar
91
M. Liu, Y. Zhang, H. Fu, Y. Qin, A. Ding, E.M. Yeatman
A seesaw-inspired bistable energy harvester with adjustable potential wells for self-powered internet of train monitoring
Appl. Energy, 337 (2023), Article 120908, 10.1016/j.apenergy.2023.120908
View PDF View article View in Scopus Google Scholar
92
L. Jin, S.L. Zhang, S. Xu, H. Guo, W. Yang, Z.L. Wang
Free-Fixed Rotational Triboelectric Nanogenerator for Self-Powered Real-Time Wheel Monitoring
Adv. Mater. Technol., 6 (2021), Article 2000918, 10.1002/admt.202000918
View in Scopus Google Scholar
93
A.M. Tairab, H. Wang, D. Hao, A. Azam, A. Ahmed, Z. Zhang
A hybrid multimodal energy harvester for self-powered wireless sensors in the railway
Energy Sustain Dev., 68 (2022), pp. 150-169, 10.1016/j.esd.2022.03.012
View PDF View article View in Scopus Google Scholar
94
Y. Zhao, X. Yu, M. Chen, M. Zhang, Y. Chen, X. Niu, X. Sha, Z. Zhan, W.J. Li
Continuous Monitoring of Train Parameters Using IoT Sensor and Edge Computing
IEEE Sensor. J., 21 (2021), pp. 15458-15468, 10.1109/JSEN.2020.3026643
View in Scopus Google Scholar
95
M. Gao, Y. Li, J. Lu, Y. Wang, P. Wang, L. Wang
Condition monitoring of urban rail transit by local energy harvesting
Int. J. Distributed Sens. Netw., 14 (2018), Article 155014771881446, 10.1177/1550147718814469
Google Scholar
96
Z. Chen, Y. Gao, J. Liang
LOPdM: A Low-Power On-Device Predictive Maintenance System Based on Self-Powered Sensing and TinyML
IEEE Trans. Instrum. Meas., 72 (2023), pp. 1-13, 10.1109/TIM.2023.3308251
Google Scholar
97
R. Li, D. Wei, Z. Wang
Synergizing Machine Learning Algorithm with Triboelectric Nanogenerators for Advanced Self-Powered Sensing Systems
Nanomaterials, 14 (2024), p. 165, 10.3390/nano14020165
View in Scopus Google Scholar
98
V. Fragnelli, S. Sanguineti
A game theoretic model for re-optimizing a railway timetable
Eur. Transp. Res. Rev., 6 (2014), pp. 113-125, 10.1007/s12544-013-0116-y
View in Scopus Google Scholar
99
M. Yin, K. Li, X. Cheng
A review on artificial intelligence in high-speed rail
Transportation Safety and Environment, 2 (2020), pp. 247-259, 10.1093/tse/tdaa022
View in Scopus Google Scholar
100
L. Zhu, C. Chen, H. Wang, F.R. Yu, T. Tang
Machine Learning in Urban Rail Transit Systems: A Survey
IEEE Trans. Intell. Transport. Syst., 25 (2024), pp. 2182-2207, 10.1109/TITS.2023.3319135
View in Scopus Google Scholar
101
J. Alzubi, A. Nayyar, A. Kumar
Machine Learning from Theory to Algorithms: An Overview
Journal of Physics: Conference Series, 1142, IOP Publishing (2018), Article 012012, 10.1088/1742-6596/1142/1/012012
View in Scopus Google Scholar
102
P. Singh, M.A. Dulebenets, J. Pasha, E.D.R.S. Gonzalez, Y.-Y. Lau, R. Kampmann
Deployment of Autonomous Trains in Rail Transportation: Current Trends and Existing Challenges
IEEE Access, 9 (2021), pp. 91427-91461, 10.1109/ACCESS.2021.3091550
View in Scopus Google Scholar
103
J. Leukel, J. González, M. Riekert
Adoption of machine learning technology for failure prediction in industrial maintenance: A systematic review
J. Manuf. Syst., 61 (2021), pp. 87-96, 10.1016/j.jmsy.2021.08.012
View PDF View article View in Scopus Google Scholar
104
M. Mićić, L. Brajović, L. Lazarević, Z. Popović
Inspection of RCF rail defects – Review of NDT methods
Mech. Syst. Signal Process., 182 (2023), Article 109568, 10.1016/j.ymssp.2022.109568
View PDF View article View in Scopus Google Scholar
105
A. Peinado Gonzalo, M. Entezami, P. Weston, C. Roberts, F. Pedro Garcia Marquez
Railway Track and Vehicle Onboard Monitoring: A Review
E3S Web Conf., 409 (2023), Article 02014, 10.1051/e3sconf/202340902014
View in Scopus Google Scholar
106
J.M. Castillo-Mingorance, M. Sol-Sánchez, F. Moreno-Navarro, M.C. Rubio-Gámez
A Critical Review of Sensors for the Continuous Monitoring of Smart and Sustainable Railway Infrastructures
Sustainability, 12 (2020), p. 9428, 10.3390/su12229428
Google Scholar
107
A.H. Alavi, H. Hasni, N. Lajnef, K. Chatti
Continuous health monitoring of pavement systems using smart sensing technology
Construct. Build. Mater., 114 (2016), pp. 719-736, 10.1016/j.conbuildmat.2016.03.128
View PDF View article View in Scopus Google Scholar
108
C. Chellaswamy, L. Balaji, A. Vanathi, L. Saravanan
IoT based rail track health monitoring and information system
2017 International conference on Microelectronic Devices, Circuits and Systems (ICMDCS), IEEE (2017), pp. 1-6, 10.1109/ICMDCS.2017.8211548
Google Scholar
109
Y. Sun, Y. Wang, S. Tian, S. Huo, M. Hu, F. Wu, Q. Yi, B. An, P. Wang, M. Gao
Energy Self-Sufficient Rail Corrugation Identification by a Multistable Piezo-Electro-Magnet Coupled Energy Transducer
IEEE Trans. Instrum. Meas., 72 (2023), pp. 1-13, 10.1109/TIM.2023.3309382
Google Scholar
110
Y. Sun, P. Wang, J. Lu, J. Xu, P. Wang, S. Xie, Y. Li, J. Dai, B. Wang, M. Gao
Rail corrugation inspection by a self-contained triple-repellent electromagnetic energy harvesting system
Appl. Energy, 286 (2021), Article 116512, 10.1016/j.apenergy.2021.116512
View PDF View article View in Scopus Google Scholar
111
T. Wang, Y. He, B. Li, T. Shi
Transformer Fault Diagnosis Using Self-Powered RFID Sensor and Deep Learning Approach
IEEE Sensor. J., 18 (2018), pp. 6399-6411, 10.1109/JSEN.2018.2844799
View in Scopus Google Scholar
112
H. Zhong, L. Liu, J. Wang, Q. Fu, B. Yi
A real-time railway fastener inspection method using the lightweight depth estimation network
Measurement, 189 (2022), Article 110613, 10.1016/j.measurement.2021.110613
View PDF View article View in Scopus Google Scholar
113
J. Xu, X. Wei, R. Li, S. Kong, Z. Wu, Z.L. Wang
A Capsule-Shaped Triboelectric Nanogenerator for Self-Powered Health Monitoring of Traffic Facilities
ACS Mater. Lett., 4 (2022), pp. 1630-1637, 10.1021/acsmaterialslett.2c00477
View in Scopus Google Scholar
114
C. Tarawneh, J.A. Aranda, V.V. Hernandez, C.J. Ramirez
An Analysis of the Efficacy of Wayside Hot-Box Detector Data
2018 Joint Rail Conference, American Society of Mechanical Engineers (2018), 10.1115/JRC2018-6218
Google Scholar
115
A.H. Alavi, H. Hasni, P. Jiao, W. Borchani, N. Lajnef
Fatigue cracking detection in steel bridge girders through a self-powered sensing concept
J. Constr. Steel Res., 128 (2017), pp. 19-38, 10.1016/j.jcsr.2016.08.002
View PDF View article View in Scopus Google Scholar
116
Z. Li, B. Cai, J. Liu, D. Lu
Modelling and performance analysis of Balise under dynamic energy harvesting in high-speed railway
IET Intell. Transp. Syst., 16 (2022), pp. 1504-1520, 10.1049/itr2.12228
View in Scopus Google Scholar
117
X. Lu, H. Li, X. Zhang, B. Gao, T. Cheng
Magnetic-assisted self-powered acceleration sensor for real-time monitoring vehicle operation and collision based on triboelectric nanogenerator
Nano Energy, 96 (2022), Article 107094, 10.1016/j.nanoen.2022.107094
View PDF View article View in Scopus Google Scholar
118
S. Kinden, Z. Batmaz
Vehicle Classification Using Deep Learning-Assisted Triboelectric Sensor
Arabian J. Sci. Eng., 49 (2023), pp. 6657-6673, 10.1007/s13369-023-08394-4
Google Scholar
119
H. Zhang, Y. Zhou
AI-based modeling and data-driven identification of moving load on continuous beams
Fundam. Res., 3 (2023), pp. 796-803, 10.1016/j.fmre.2022.02.013
View PDF View article View in Scopus Google Scholar
120
E. Bernal, M. Spiryagin, C. Cole
Onboard Condition Monitoring Sensors, Systems and Techniques for Freight Railway Vehicles: A Review
IEEE Sensor. J., 19 (2019), pp. 4-24, 10.1109/JSEN.2018.2875160
View in Scopus Google Scholar
121
O. Brignole, C. Cavalletti, A. Maresca, N. Mazzino, M. Balato, A. Buonomo, L. Costanzo, M. Giorgio, R. Langella, A.L. Schiavo, et al.
Resonant electromagnetic vibration harvesters feeding sensor nodes for real-time diagnostics and monitoring in railway vehicles for goods transportation: A numerical-experimental analysis
2016 IEEE International Power Electronics and Motion Control Conference (PEMC), IEEE (2016), pp. 456-461, 10.1109/EPEPEMC.2016.7752040
View in Scopus Google Scholar
122
D. Huang, S. Li, N. Qin, Y. Zhang
Fault Diagnosis of High-Speed Train Bogie Based on the Improved-CEEMDAN and 1-D CNN Algorithms
IEEE Trans. Instrum. Meas., 70 (2021), pp. 1-11, 10.1109/TIM.2020.3047922
View in Scopus Google Scholar
123
Z. Fang, Z. Zhou, M. Yi, Z. Zhang, X. Luo, A. Ahmed
A roller-bearing-based triboelectric nanosensor for freight train synergistic maintenance in smart transportation
Nano Energy, 106 (2023), Article 108089, 10.1016/j.nanoen.2022.108089
View PDF View article View in Scopus Google Scholar
124
Q. Zheng, Y. Hou, H. Yang, P. Tan, H. Shi, Z. Xu, Z. Ye, N. Chen, X. Qu, X. Han, et al.
Towards a sustainable monitoring: A self-powered smart transportation infrastructure skin
Nano Energy, 98 (2022), Article 107245, 10.1016/j.nanoen.2022.107245
View PDF View article View in Scopus Google Scholar
125
J. Yang, Y. Sun, J. Zhang, B. Chen, Z.L. Wang
3D-printed bearing structural triboelectric nanogenerator for intelligent vehicle monitoring
Cell Rep. Phys. Sci., 2 (2021), Article 100666, 10.1016/j.xcrp.2021.100666
View PDF View article View in Scopus Google Scholar
126
H. Askari, Z. Saadatnia, A. Khajepour, M.B. Khamesee, J. Zu
A Triboelectric Self-Powered Sensor for Tire Condition Monitoring: Concept, Design, Fabrication, and Experiments
Adv. Eng. Mater., 19 (2017), Article 1700318, 10.1002/adem.201700318
View in Scopus Google Scholar
127
E. Bernal, M. Spiryagin, C. Cole
Ultra-Low Power Sensor Node for On-Board Railway Wagon Monitoring
IEEE Sensor. J., 20 (2020), pp. 15185-15192, 10.1109/JSEN.2020.3011132
View in Scopus Google Scholar
128
S.K. Lai, C. Wang, L.H. Zhang, Y.Q. Ni
Realizing a Self-powered Real-time Monitoring System on High-speed Trains
Internoise, 263 (2021), pp. 434-441, 10.3397/IN-2021-1476
Google Scholar
129
M. Jiang, B. Li, W. Jia, Z. Zhu
Predicting output performance of triboelectric nanogenerators using deep learning model
Nano Energy, 93 (2022), Article 106830, 10.1016/j.nanoen.2021.106830
View PDF View article View in Scopus Google Scholar
130
J. Wang, Z. Shi, H. Xiang, G. Song
Modeling on energy harvesting from a railway system using piezoelectric transducers
Smart Mater. Struct., 24 (2015), Article 105017, 10.1088/0964-1726/24/10/105017
View in Scopus Google Scholar
131
L. Tian, Y. Huang, S. Liu, S. Sun, J. Deng, H. Zhao
Application of photovoltaic power generation in rail transit power supply system under the background of energy low carbon transformation
Alex. Eng. J., 60 (2021), pp. 5167-5174, 10.1016/j.aej.2021.04.008
View PDF View article View in Scopus Google Scholar
132
Y. Pang, X. Zhu, Y. Yu, S. Liu, Y. Chen, Y. Feng
Waterbomb-origami inspired triboelectric nanogenerator for smart pavement-integrated traffic monitoring
Nano Res., 15 (2022), pp. 5450-5460, 10.1007/s12274-022-4152-6
View in Scopus Google Scholar
133
D. Zhu, X. Guo, H. Li, Z. Yuan, X. Zhang, T. Cheng
Self-powered flow sensing for automobile based on triboelectric nanogenerator with magnetic field modulation mechanism
Nano Energy, 108 (2023), Article 108233, 10.1016/j.nanoen.2023.108233
View PDF View article View in Scopus Google Scholar
134
H. Behrooz, Y.M. Hayeri
Machine Learning Applications in Surface Transportation Systems: A Literature Review
Appl. Sci., 12 (2022), p. 9156, 10.3390/app12189156
View in Scopus Google Scholar
135
A.F. Villán
Facial attributes recognition using computer vision to detect drowsiness and distraction in drivers
ELCVIA Electron. Lett. Comput. Vis. Image Anal., 16 (2017), pp. 25-28, 10.5565/rev/elcvia.1134
Google Scholar
136
S. Fu, Z. Yang, Y. Ma, Z. Li, L. Xu, H. Zhou
Advancements in the Intelligent Detection of Driver Fatigue and Distraction: A Comprehensive Review
Appl. Sci., 14 (2024), p. 3016, 10.3390/app14073016
View in Scopus Google Scholar
137
H. Li, M. Liang, K. Niu, Y. Zhang
A Human-Machine Trust Evaluation Method for High-Speed Train Drivers Based on Multi-Modal Physiological Information
Int. J. Hum. Comput. Interact., 0 (2024), pp. 1-18, 10.1080/10447318.2024.2327188
Google Scholar
138
X. Lu, H. Zhang, X. Zhao, H. Yang, L. Zheng, W. Wang, C. Sun
Triboelectric nanogenerator based self-powered sensor with a turnable sector structure for monitoring driving behavior
Nano Energy, 89 (2021), Article 106352, 10.1016/j.nanoen.2021.106352
View PDF View article View in Scopus Google Scholar
139
L. Chen, K. Yuan, S. Chen, Y. Huang, H. Askari, N. Yu, J. Mo, N. Xu, M. Wu, H. Chen, et al.
Triboelectric nanogenerator sensors for intelligent steering wheel aiming at automated driving
Nano Energy, 113 (2023), Article 108575, 10.1016/j.nanoen.2023.108575
View PDF View article View in Scopus Google Scholar
140
Z. Xie, Z. Zeng, Y. Wang, W. Yang, Y. Xu, X. Lu, T. Cheng, H. Zhao, Z.L. Wang
Novel sweep-type triboelectric nanogenerator utilizing single freewheel for random triggering motion energy harvesting and driver habits monitoring
Nano Energy, 68 (2020), Article 104360, 10.1016/j.nanoen.2019.104360
View PDF View article View in Scopus Google Scholar
141
X. Lu, B. Leng, H. Li, X. Lv, X. Zhang, T. Qu, S. Li, Y. Wang, J. Wen, B. Zhang, T. Cheng
An Intelligent Driving Monitoring System Utilizing Pedal Motion Sensor Integrated with Triboelectric-Electromagnetic Hybrid Generator and Machine Learning
Adv. Mater. Technol., 9 (2024), Article 2301706, 10.1002/admt.202301706
View in Scopus Google Scholar
142
X. Zhang, Z. Yang, S. Yang, X. Zhang, H. Li, X. Lu, B. Zhang, Z.L. Wang, T. Cheng
Deep learning assisted three triboelectric driving operation sensors for driver training and behavior monitoring
Mater. Today, 72 (2024), pp. 47-56, 10.1016/j.mattod.2023.11.007
View PDF View article Google Scholar
143
F. Luo, B. Chen, X. Ran, W. Ouyang, Y. Yao, L. Shang
Wearable and self-powered triboelectric sensors based on NaCl/PVA hydrogel for driver multidimensional information monitoring
Nano Energy, 118 (2023), Article 109035, 10.1016/j.nanoen.2023.109035
View PDF View article View in Scopus Google Scholar
144
J. Chen, L. Kong, Z. Fang, R. Zou, J. Wu, H. Tang, Z. Zhang
A self-powered and self-sensing driver behavior detection system for smart transportation
Nano Energy, 122 (2024), Article 109327, 10.1016/j.nanoen.2024.109327
View PDF View article Google Scholar
145
A. S Aghaei, B. Donmez, C.C. Liu, D. He, G. Liu, K.N. Plataniotis, H.-Y.W. Chen, Z. Sojoudi
Smart Driver Monitoring: When Signal Processing Meets Human Factors: In the driver’s seat
IEEE Signal Process. Mag., 33 (2016), pp. 35-48, 10.1109/MSP.2016.2602379
Google Scholar
146
J. Yang, S. Liu, Y. Meng, W. Xu, S. Liu, L. Jia, G. Chen, Y. Qin, M. Han, X. Li
Self-Powered Tactile Sensor for Gesture Recognition Using Deep Learning Algorithms
ACS Appl. Mater. Interfaces, 14 (2022), pp. 25629-25637, 10.1021/acsami.2c01730
View in Scopus Google Scholar
147
H. Zhang, H. Tan, W. Wang, Z. Li, F. Chen, X. Jiang, X. Lu, Y. Hu, L. Li, J. Zhang, et al.
Real-Time Non-Driving Behavior Recognition Using Deep Learning-Assisted Triboelectric Sensors in Conditionally Automated Driving
Adv. Funct. Mater., 33 (2023), Article 2210580, 10.1002/adfm.202210580
View in Scopus Google Scholar
148
L. Dong, H. Zhang, J. Yu, G. Hu
Energy harvesting potential assessment and systematic design for energy-regenerative shock absorbers on railway freight wagons
J. Intell. Mater. Syst. Struct., 35 (2024), pp. 270-290, 10.1177/1045389X231200146
View in Scopus Google Scholar
149
H. Fu, W. Song, Y. Qin, E.M. Yeatman
Broadband Vibration Energy Harvesting from Underground Trains for Self-Powered Condition Monitoring
2019 19th International Conference on Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS), IEEE (2019), pp. 1-5, 10.1109/PowerMEMS49317.2019.93285904627
View in Scopus Google Scholar
150
J. Zuo, L. Dong, J. Ding, X. Wang, P. Diao, J. Yu
Design and validation of a self-powered device for wireless electronically controlled pneumatic brake and onboard monitoring in freight wagons
Energy Convers. Manag., 239 (2021), Article 114229, 10.1016/j.enconman.2021.114229
View PDF View article View in Scopus Google Scholar
151
T. Guo, J. Zhao, W. Liu, G. Liu, Y. Pang, T. Bu, F. Xi, C. Zhang, X. Li
Self-Powered Hall Vehicle Sensors Based on Triboelectric Nanogenerators
Adv. Mater. Technol., 3 (2018), Article 1800140, 10.1002/admt.201800140
View in Scopus Google Scholar
152
J. Lian, X. Ding, D. Cai, M. Yue
Intelligent adaptive control method for key parameters of vibration rolling during high-speed railway subgrade compaction
Transp. Geotech., 41 (2023), Article 101000, 10.1016/j.trgeo.2023.101000
View PDF View article View in Scopus Google Scholar
153
Y. Sun, H. Qiang, J. Xu, G. Lin
Internet of Things-Based Online Condition Monitor and Improved Adaptive Fuzzy Control for a Medium-Low-Speed Maglev Train System
IEEE Trans. Ind. Inf., 16 (2020), pp. 2629-2639, 10.1109/TII.2019.2938145
View in Scopus Google Scholar
154
P. Wang, T. Mei, J. Zhang, H. Li
Self-Powered Active Lateral Secondary Suspension for Railway Vehicles
IEEE Trans. Veh. Technol., 65 (2016), pp. 1121-1129, 10.1109/TVT.2015.2407575
View in Scopus Google Scholar
155
P. Wang, H. Li, J. Zhang, T. Mei
An analytical design approach for self-powered active lateral secondary suspensions for railway vehicles
Veh. Syst. Dyn., 53 (2015), pp. 1439-1454, 10.1080/00423114.2015.1050964
View in Scopus Google Scholar
156
V.A. Morais, J.L. Afonso, A.P. Martins
Towards Smart Railways: A Charging Strategy for On-Board Energy Storage Systems
P.J.G. Afonso, L. João, Monteiro (Eds.), Sustainable Energy for Smart Cities, Springer International Publishing (2020), pp. 33-46, 10.1007/978-3-030-45694-8_3
View in Scopus Google Scholar
157
Y. Fan, L. Zhang, D. Li, Z. Wang
Progress in self-powered, multi-parameter, micro sensor technologies for power metaverse and smart grids
Nano Energy, 118 (2023), Article 108959, 10.1016/j.nanoen.2023.108959
View PDF View article View in Scopus Google Scholar
158
Y. Liao, W. Li, K. Wang, J. Guo, Y. Shen, Q. Wang, Y. Zhang, C. Wu, X. Zhou, T. Guo, T.W. Kim
TENG-inspired LED-in-capacitors for smart self-powered high-voltage monitoring and high-sensitivity demodulation of power-line communications
Nano Energy, 102 (2022), Article 107698, 10.1016/j.nanoen.2022.107698
View PDF View article View in Scopus Google Scholar
159
A. Hussain, V.-H. Bui, H.-M. Kim
Microgrids as a resilience resource and strategies used by microgrids for enhancing resilience
Appl. Energy, 240 (2019), pp. 56-72, 10.1016/j.apenergy.2019.02.055
View PDF View article View in Scopus Google Scholar
160
R.S. Peres, X. Jia, J. Lee, K. Sun, A.W. Colombo, J. Barata
Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook
IEEE Access, 8 (2020), pp. 220121-220139, 10.1109/ACCESS.2020.3042874
View in Scopus Google Scholar
161
Y. Tang, X. Dai, C. Zhao, Q. Cheng, Y. Lv
Large Language Model-Driven Urban Traffic Signal Control
2024 Australian & New Zealand Control Conference (ANZCC), IEEE (2024), pp. 67-71, 10.1109/ANZCC59813.2024.10432823
View in Scopus Google Scholar
162
H. Wang, Y.-F. Li
Large Language Model Empowered by Domain-Specific Knowledge Base for Industrial Equipment Operation and Maintenance
2023 5th International Conference on System Reliability and Safety Engineering (SRSE), IEEE (2023), pp. 474-479, 10.1109/SRSE59585.2023.10336112
View in Scopus Google Scholar
163
H. Wang, Y.-F. Li
Large-Scale Language Models for PHM in Railway Systems - Potential Applications, Limitations, and Solutions
J. Yang, D. Yao, L. Jia, Y. Qin, Z. Liu, L. Diao (Eds.), Proceedings of the 6th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2023, Springer Nature Singapore (2024), pp. 591-599, 10.1007/978-981-99-9311-6_59
Google Scholar
164
F. Tao, H. Zhang, A. Liu, A.Y.C. Nee
Digital Twin in Industry: State-of-the-Art
IEEE Trans. Ind. Inf., 15 (2019), pp. 2405-2415, 10.1109/TII.2018.2873186
View in Scopus Google Scholar
165
L. Zhao, G. Han, Z. Li, L. Shu
Intelligent Digital Twin-Based Software-Defined Vehicular Networks
IEEE Netw., 34 (2020), pp. 178-184, 10.1109/MNET.011.1900587
View in Scopus Google Scholar
166
Z. Tang, L. Ling, T. Zhang, Y. Hu, K. Wang, W. Zhai
Towards digital twin trains: implementing a cloud-based framework for railway vehicle dynamics simulation
Int. J. Real. Ther., 0 (2024), pp. 1-24, 10.1080/23248378.2024.2355578
Google Scholar
167
S. Ahmad, M. Spiryagin, Q. Wu, E. Bernal, Y. Sun, C. Cole, B. Makin
Development of a Digital Twin for prediction of rail surface damage in heavy haul railway operations
Veh. Syst. Dyn., 62 (2024), pp. 41-66, 10.1080/00423114.2023.2237620
Google Scholar
168
Y. Yu, L. Guo, K. Deng, H. Gao, B. Wang
Enabling Safety Guarantees of Urban Rail Transit: A New Digital Twin Framework for Data-Model Driven Track Condition Diagnosis
J. Yang, D. Yao, L. Jia, Y. Qin, Z. Liu, L. Diao (Eds.), Proceedings of the 6th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2023, Springer Nature Singapore (2024), pp. 629-637, 10.1007/978-981-99-9319-2_70
View in Scopus Google Scholar
169
C. Liu, D. He, Z. Wei, C. He, Z. Lao, S. Shan
Personalized fault diagnosis of rolling bearings in trains based on digital twin
Meas. Sci. Technol., 34 (2023), Article 125131, 10.1088/1361-6501/acf517
View in Scopus Google Scholar
170
X. Wu, W. Lian, M. Zhou, H. Song, H. Dong
A Digital Twin-Based Fault Diagnosis Framework for Bogies of High-Speed Trains
IEEE J. Radio Freq. Identif., 7 (2023), pp. 203-207, 10.1109/JRFID.2022.3216331
View in Scopus Google Scholar
171
N. Bosso, M. Magelli, R. Trinchero, N. Zampieri
Application of machine learning techniques to build digital twins for long train dynamics simulations
Veh. Syst. Dyn., 62 (2024), pp. 21-40, 10.1080/00423114.2023.2174885
Google Scholar
172
Y. Wang, G. Zhang, R. Chen, Z. Liu, R. Qiu
Analysis of digital twin application of urban rail power supply system for energy saving
Proceedings 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence, DTPI 2021, Institute of Electrical and Electronics Engineers Inc (2021), pp. 29-32, 10.1109/DTPI52967.2021.9540127
Google Scholar
173
A. Bussmann-Holder, H. Keller
High-temperature superconductors: underlying physics and applications
Z. Naturforsch. B Chem. Sci., 75 (2020), pp. 3-14, 10.1515/znb-2019-0103
Google Scholar
174
A. Allais, J.-M. Saugrain, B. West, N. Lallouet, H. Caron, D. Ferandelle, L. Terrien, G. Bouvier, G. Hajiri, K. Berger, L. Quéval
SuperRail–World-First HTS Cable to be Installed on a Railway Network in France
IEEE Trans. Appl. Supercond., 34 (2024), pp. 1-7, 10.1109/TASC.2024.3356450
View in Scopus Google Scholar
175
L. Wang, Z. Deng, H. Wang, H. Li, K. Li, S. Ma
Dynamic Responses of HTS Maglev System Under Track Random Irregularity
IEEE Trans. Appl. Supercond., 30 (2020), pp. 1-7, 10.1109/TASC.2020.2976995
Google Scholar
176
Y. Fu, H. Li, L. Wang, P. Zhang, K. Zhang, Z. Deng
Dynamic simulation of HTS maglev vehicle/track coupled based on discrete elastic support track model
Physica C (Amsterdam, Neth.), 592 (2022), Article 1353997, 10.1016/j.physc.2021.1353997
View PDF View article View in Scopus Google Scholar
177
Q. Li, Z. Deng, L. Wang, H. Li, J. Zhang, E.F. Rodriguez
Active vibration control of secondary suspension based on high-temperature superconducting maglev vehicle system
Physica C (Amsterdam, Neth.), 585 (2021), Article 1353872, 10.1016/j.physc.2021.1353872
View PDF View article View in Scopus Google Scholar
178
K.M. Hou, X. Diao, H. Shi, H. Ding, H. Zhou, C. de Vaulx
Trends and Challenges in AIoT/IIoT/IoT Implementation
Sensors, 23 (2023), p. 5074, 10.3390/s23115074
View in Scopus Google Scholar
179
R. De Fazio, M. De Giorgi, D. Cafagna, C. Del-Valle-Soto, P. Visconti
Energy Harvesting Technologies and Devices from Vehicular Transit and Natural Sources on Roads for a Sustainable Transport: State-of-the-Art Analysis and Commercial Solutions
Energies, 16 (2023), p. 3016, 10.3390/en16073016
View in Scopus Google Scholar
180
H. Ryu, H.J. Yoon, S.W. Kim
Hybrid Energy Harvesters: Toward Sustainable Energy Harvesting
Adv. Mater., 31 (2019), p. e1802898, 10.1002/adma.201802898
Google Scholar
181
R. Bokade, A. Navato, R. Ouyang, X. Jin, C.-A. Chou, S. Ostadabbas, A.V. Mueller
A cross-disciplinary comparison of multimodal data fusion approaches and applications: Accelerating learning through trans-disciplinary information sharing
Expert Syst. Appl., 165 (2021), Article 113885, 10.1016/j.eswa.2020.113885
View PDF View article View in Scopus Google Scholar
182
Q. Shi, B. Dong, T. He, Z. Sun, J. Zhu, Z. Zhang, C. Lee
Progress in wearable electronics/photonics—Moving toward the era of artificial intelligence and internet of things
InfoMat, 2 (2020), pp. 1131-1162, 10.1002/inf2.12122
View in Scopus Google Scholar
183
X. Yang, Z. Luo, Z. Huang, Y. Zhao, Z. Xue, Y. Wang, W. Liu, S. Liu, H. Zhang, K. Xu, et al.
Development Status and Prospects of Artificial Intelligence in the Field of Energy Conversion Materials
Front. Energy Res., 8 (2020), p. 167, 10.3389/fenrg.2020.00167
Google Scholar

Cited by (0)

[1] 1
B. Jiang, C. Tian, J. Deng, Z. Zhu
China’s railway train speed, density and weight in developing
J. Hous. Built Environ., 1 (2022), pp. 131-147, 10.1108/RS-04-2022-0009
View at publisherGoogle Scholar

[2] 2
D.F. Dominković, I. Bačeković, A.S. Pedersen, G. Krajačić
The future of transportation in sustainable energy systems: Opportunities and barriers in a clean energy transition
Renew. Sustain. Energy Rev., 82 (2018), pp. 1823-1838, 10.1016/j.rser.2017.06.117
View PDF View article View in Scopus Google Scholar

[3] 3
N. Bosso, M. Magelli, N. Zampieri
Application of low-power energy harvesting solutions in the railway field: a review
Veh. Syst. Dyn., 59 (2021), pp. 841-871, 10.1080/00423114.2020.1726973
View in Scopus Google Scholar

[4] 4
N. Xu, J. Han, Y. Xiong, Z.L. Wang, Q. Sun
TENG Applications in Transportation and Surrounding Emergency Management
Adv. Sustain. Syst., 6 (2022), 10.1002/adsu.202200267
Google Scholar

[5] 5
J. Zuo, L. Dong, F. Yang, Z. Guo, T. Wang, L. Zuo
Energy harvesting solutions for railway transportation: A comprehensive review
Renew. Energy, 202 (2023), pp. 56-87, 10.1016/j.renene.2022.11.008
View PDF View article View in Scopus Google Scholar

[6] 6
R. Du, J. Xiao, S. Chang, L. Zhao, K. Wei, W. Zhang, H. Zou
Mechanical energy harvesting in traffic environment and its application in smart transportation
J. Phys. D Appl. Phys., 56 (2023), Article 373002, 10.1088/1361-6463/acdadb
View in Scopus Google Scholar

[7] 7
Y. Wang, S. Li, P. Wang, M. Gao, H. Ouyang, Q. He, P. Wang
A multifunctional electromagnetic device for vibration energy harvesting and rail corrugation sensing
Smart Mater. Struct., 30 (2021), Article 125012, 10.1088/1361-665X/ac31c5
View in Scopus Google Scholar

[8] 8
F. Ksica, O. Rubes, J. Kovar, J. Chalupa, Z. Hadas
Smart Sensing System for Railway Monitoring
2022 20th International Conference on Mechatronics - Mechatronika (ME), IEEE (2022), pp. 1-6, 10.1109/ME54704.2022.9983339
Google Scholar

[9] 9
H. Salehi, R. Burgueño, S. Chakrabartty, N. Lajnef, A.H. Alavi
A comprehensive review of self-powered sensors in civil infrastructure: State-of-the-art and future research trends
Eng. Struct., 234 (2021), Article 111963, 10.1016/j.engstruct.2021.111963
View PDF View article View in Scopus Google Scholar

[10] 10
P. Singh, Z. Elmi, V. Krishna Meriga, J. Pasha, M.A. Dulebenets
Internet of Things for sustainable railway transportation: Past, present, and future
Cleaner Logistics and Supply Chain, 4 (2022), Article 100065, 10.1016/j.clscn.2022.100065
View PDF View article View in Scopus Google Scholar

[11] 11
X. Zhao, H. Askari, J. Chen
Nanogenerators for smart cities in the era of 5G and Internet of Things
Joule, 5 (2021), pp. 1391-1431, 10.1016/j.joule.2021.03.013
View PDF View article View in Scopus Google Scholar

[12] 12
D. Yang, E. Cui, H. Wang, H. Zhang
EH-Edge--An Energy Harvesting-Driven Edge IoT Platform for Online Failure Prediction of Rail Transit Vehicles: A case study of a cloud, edge, and end device collaborative computing paradigm
IEEE Veh. Technol. Mag., 16 (2021), pp. 95-103, 10.1109/MVT.2021.3053193
View in Scopus Google Scholar

[13] 13
F. Zanelli, M. Mauri, F. Castelli-Dezza, E. Sabbioni, D. Tarsitano, N. Debattisti
Energy Autonomous Wireless Sensor Nodes for Freight Train Braking Systems Monitoring
Sensors, 22 (2022), p. 1876, 10.3390/s22051876
View in Scopus Google Scholar

[14] 14
R. Dirnfeld, F. Flammini, S. Marrone, R. Nardone, V. Vittorini
Low-Power Wide-Area Networks in Intelligent Transportation: Review and Opportunities for Smart-Railways
2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) (IEEE) (2020), pp. 1-7, 10.1109/ITSC45102.2020.9294535
Google Scholar

[15] 15
J. Zhang, Y. Yu, L. Zhang, J. Chen, X. Wang, X. Wang
Dig information of nanogenerators by machine learning
Nano Energy, 114 (2023), Article 108656, 10.1016/j.nanoen.2023.108656
View PDF View article View in Scopus Google Scholar

[16] 16
R. Tang, L. De Donato, N. Besinović, F. Flammini, R.M.P. Goverde, Z. Lin, R. Liu, T. Tang, V. Vittorini, Z. Wang
A literature review of Artificial Intelligence applications in railway systems
Transport. Res. C Emerg. Technol., 140 (2022), Article 103679, 10.1016/j.trc.2022.103679
View PDF View article View in Scopus Google Scholar

[17] 17
X. Gibert, V.M. Patel, R. Chellappa
Deep Multitask Learning for Railway Track Inspection
IEEE Trans. Intell. Transport. Syst., 18 (2017), pp. 153-164, 10.1109/TITS.2016.2568758
View in Scopus Google Scholar

[18] 18
M. Akhtar, S. Moridpour
A Review of Traffic Congestion Prediction Using Artificial Intelligence
J. Adv. Transport., 2021 (2021), pp. 1-18, 10.1155/2021/8878011
Google Scholar

[19] 19
A. Boukerche, J. Wang
A performance modeling and analysis of a novel vehicular traffic flow prediction system using a hybrid machine learning-based model
Ad Hoc Netw., 106 (2020), Article 102224, 10.1016/j.adhoc.2020.102224
View PDF View article View in Scopus Google Scholar

[20] 20
C. Wen, P. Huang, Z. Li, J. Lessan, L. Fu, C. Jiang, X. Xu
Train Dispatching Management With Data- Driven Approaches: A Comprehensive Review and Appraisal
IEEE Access, 7 (2019), pp. 114547-114571, 10.1109/ACCESS.2019.2935106
View in Scopus Google Scholar

[21] 21
A. Alagumalai, W. Shou, O. Mahian, M. Aghbashlo, M. Tabatabaei, S. Wongwises, Y. Liu, J. Zhan, A. Torralba, J. Chen, et al.
Self-powered sensing systems with learning capability
J. Inflamm. Res., 15 (2022), pp. 3083-3094, 10.1016/j.joule.2022.06.001
Google Scholar

[22] 22
N. Besinovic, L. De Donato, F. Flammini, R.M.P. Goverde, Z. Lin, R. Liu, S. Marrone, R. Nardone, T. Tang, V. Vittorini
Artificial Intelligence in Railway Transport: Taxonomy, Regulations, and Applications
IEEE Trans. Intell. Transport. Syst., 23 (2022), pp. 14011-14024, 10.1109/TITS.2021.3131637
View in Scopus Google Scholar

[23] 23
N. Zhao, H. Zhang, X. Yang, J. Yan, F. You
Emerging information and communication technologies for smart energy systems and renewable transition
Adv. Appl. Energy, 9 (2023), Article 100125, 10.1016/j.adapen.2023.100125
View PDF View article View in Scopus Google Scholar

[24] 24
C. Laiton-Bonadiez, J.W. Branch-Bedoya, J. Zapata-Cortes, E. Paipa-Sanabria, M. Arango-Serna
Industry 4.0 Technologies Applied to the Rail Transportation Industry: A Systematic Review
Sensors, 22 (2022), p. 2491, 10.3390/s22072491
View in Scopus Google Scholar

[25] 25
G. Li, S.W. Or, K.W. Chan
Intelligent Energy-Efficient Train Trajectory Optimization Approach Based on Supervised Reinforcement Learning for Urban Rail Transits
IEEE Access, 11 (2023), pp. 31508-31521, 10.1109/ACCESS.2023.3261900
View in Scopus Google Scholar

[26] 26
T. Zhang, T. Yang, M. Zhang, C.R. Bowen, Y. Yang
Recent Progress in Hybridized Nanogenerators for Energy Scavenging
iScience, 23 (2020), Article 101689, 10.1016/j.isci.2020.101689
View PDF View article View in Scopus Google Scholar

[27] 27
Z. Wang, W. Wang, F. Gu, C. Wang, Q. Zhang, G. Feng, A.D. Ball
On-rotor electromagnetic energy harvester for powering a wireless condition monitoring system on bogie frames
Energy Convers. Manag., 243 (2021), Article 114413, 10.1016/j.enconman.2021.114413
View PDF View article View in Scopus Google Scholar

[28] 28
Y. Du, Q. Tang, W. He, W. Liu, Z. Wang, H. Wu, G. Li, H. Guo, Z. Li, Y. Peng, C. Hu
Harvesting ambient mechanical energy by multiple mode triboelectric nanogenerator with charge excitation for self-powered freight train monitoring
Nano Energy, 90 (2021), Article 106543, 10.1016/j.nanoen.2021.106543
View PDF View article View in Scopus Google Scholar

[29] 29
Z. Wang, W. Wang, L. Tang, R. Tian, C. Wang, Q. Zhang, C. Liu, F. Gu, A.D. Ball
A piezoelectric energy harvester for freight train condition monitoring system with the hybrid nonlinear mechanism
Mech. Syst. Signal Process., 180 (2022), Article 109403, 10.1016/j.ymssp.2022.109403
View PDF View article View in Scopus Google Scholar

[30] 30
G. Shan, D. Wang, Z.J. Chew, M. Zhu
A high-power, robust piezoelectric energy harvester for wireless sensor networks in railway applications
Sens Actuators A Phys, 360 (2023), Article 114525, 10.1016/j.sna.2023.114525
View PDF View article View in Scopus Google Scholar

[31] 31
M. Gao, J. Cong, J. Xiao, Q. He, S. Li, Y. Wang, Y. Yao, R. Chen, P. Wang
Dynamic modeling and experimental investigation of self-powered sensor nodes for freight rail transport
Appl. Energy, 257 (2020), Article 113969, 10.1016/j.apenergy.2019.113969
View PDF View article View in Scopus Google Scholar

[32] 32
Y. Pan, L. Zuo, M. Ahmadian
A half-wave electromagnetic energy-harvesting tie towards safe and intelligent rail transportation
Appl. Energy, 313 (2022), Article 118844, 10.1016/j.apenergy.2022.118844
View PDF View article View in Scopus Google Scholar

[33] 33
W. Salman, C. Fan, H. Pan, Z. Zhang, X. Wu, M. Abdelrahman, A.M. Tairab, A. Ali
A dual-kinetic energy harvester operating on the track and wheel of rail deceleration system for self-powered sensors
Smart Mater. Struct., 32 (2023), Article 125023, 10.1088/1361-665X/ad0b1a
View in Scopus Google Scholar

[34] 34
D. Hao, T. Zhang, L. Guo, Y. Feng, Z. Zhang, Y. Yuan
A High-Efficiency, Portable Solar Energy-Harvesting System Based on a Foldable-Wings Mechanism for Self-Powered Applications in Railways
Energ. Tech., 9 (2021), Article 2000794, 10.1002/ente.202000794
View in Scopus Google Scholar

[35] 35
H. Foltz, C. Tarawneh, M. Amaro, S. Thomas, D. Capitanachi Avila
Thermoelectric energy harvesting for wireless onboard rail condition monitoring
Int. J. Real. Ther., 12 (2023), pp. 514-531, 10.1080/23248378.2023.2201247
Google Scholar

[36] 36
Y. Wang, X. Zhu, T. Zhang, S. Bano, H. Pan, L. Qi, Z. Zhang, Y. Yuan
A renewable low-frequency acoustic energy harvesting noise barrier for high-speed railways using a Helmholtz resonator and a PVDF film
Appl. Energy, 230 (2018), pp. 52-61, 10.1016/j.apenergy.2018.08.080
View PDF View article Google Scholar

[37] 37
P. Lopez Diez, I. Gabilondo, E. Alarcon, F. Moll
Mechanical Energy Harvesting Taxonomy for Industrial Environments: Application to the Railway Industry
IEEE Trans. Intell. Transport. Syst., 21 (2020), pp. 2696-2706, 10.1109/TITS.2019.2924987
View in Scopus Google Scholar

[38] 38
G. Jing, M. Siahkouhi, J. Riley Edwards, M.S. Dersch, N.A. Hoult
Smart railway sleepers - a review of recent developments, challenges, and future prospects
Construct. Build. Mater., 271 (2021), Article 121533, 10.1016/j.conbuildmat.2020.121533
View PDF View article View in Scopus Google Scholar

[39] 39
S. Zheng, Z. Wu, Y. Xu, Z. Wei
Intrusion Detection of Foreign Objects in Overhead Power System for Preventive Maintenance in High-Speed Railway Catenary Inspection
IEEE Trans. Instrum. Meas., 71 (2022), pp. 1-12, 10.1109/TIM.2022.3189642
Google Scholar

[40] 40
B. Xiong, H. Wang, L. Wang, Z. Zhang, Y. Pan, T. Liu, M. Tang, G. Liu, Y. Hu
Sustainable wind barrier: Self-powered system for high-speed railway safety monitoring
Sustain. Mater. Technol., 39 (2024), Article e00848, 10.1016/j.susmat.2024.e00848
View PDF View article View in Scopus Google Scholar

[41] 41
C. Jia, H. Li, X. Zhou, Z. Fang, X. Wu, Z. Zhang
A renewable energy harvesting wind barrier based on coaxial contrarotation for self-powered applications on railways
Energy, 258 (2022), Article 124842, 10.1016/j.energy.2022.124842
Google Scholar

[42] 42
H. Pan, H. Li, T. Zhang, A.A. Laghari, Z. Zhang, Y. Yuan, B. Qian
A portable renewable wind energy harvesting system integrated S-rotor and H-rotor for self-powered applications in high-speed railway tunnels
Energy Convers. Manag., 196 (2019), pp. 56-68, 10.1016/j.enconman.2019.05.115
View PDF View article View in Scopus Google Scholar

[43] 43
P. Zheng, L. Qi, M. Sun, D. Luo, Z. Zhang
A novel wind energy harvesting system with hybrid mechanism for self-powered applications in subway tunnels
Energy, 227 (2021), Article 120446, 10.1016/j.energy.2021.120446
View PDF View article View in Scopus Google Scholar

[44] 44
W. Guo, Y. Long, Z. Bai, X. Wang, H. Liu, Z. Guo, S. Tan, H. Guo, Y. Wang, Y. Miao
Variable stiffness triboelectric nano-generator to harvest high-speed railway bridge’s vibration energy
Energy Convers. Manag., 268 (2022), Article 115969, 10.1016/j.enconman.2022.115969
View PDF View article View in Scopus Google Scholar

[45] 45
K. Huang, Y. Zhou, Z. Zhang, H. Zhang, C. Lü, J. Luo, L. Shen
A real-time quantitative acceleration monitoring method based on triboelectric nanogenerator for bridge cable vibration
Nano Energy, 118 (2023), Article 108960, 10.1016/j.nanoen.2023.108960
View PDF View article View in Scopus Google Scholar

[46] 46
O. Jo, Y.-K. Kim, J. Kim
Internet of Things for Smart Railway: Feasibility and Applications
IEEE Internet Things J., 5 (2018), pp. 482-490, 10.1109/JIOT.2017.2749401
View in Scopus Google Scholar

[47] 47
L. Jin, W. Deng, Y. Su, Z. Xu, H. Meng, B. Wang, H. Zhang, B. Zhang, L. Zhang, X. Xiao, et al.
Self-powered wireless smart sensor based on maglev porous nanogenerator for train monitoring system
Nano Energy, 38 (2017), pp. 185-192, 10.1016/j.nanoen.2017.05.018
View PDF View article View in Scopus Google Scholar

[48] 48
H. Zhang, C. Yang, Y. Yu, Y. Zhou, L. Quan, S. Dong, J. Luo
Origami-tessellation-based triboelectric nanogenerator for energy harvesting with application in road pavement
Nano Energy, 78 (2020), Article 105177, 10.1016/j.nanoen.2020.105177
View PDF View article View in Scopus Google Scholar

[49] 49
Y. Sun, Y. Yan, S. Tian, G. Liu, F. Wu, P. Wang, M. Gao
Wireless sensing in high-speed railway turnouts with battery-free materials and devices
iScience, 27 (2024), Article 108663, 10.1016/j.isci.2023.108663
View PDF View article View in Scopus Google Scholar

[50] 50
Z. Lin, C. Sun, W. Liu, E. Fan, G. Zhang, X. Tan, Z. Shen, J. Qiu, J. Yang
A self-powered and high-frequency vibration sensor with layer-powder-layer structure for structural health monitoring
Nano Energy, 90 (2021), Article 106366, 10.1016/j.nanoen.2021.106366
View PDF View article View in Scopus Google Scholar

[51] 51
Y. Meng, J. Yang, S. Liu, W. Xu, G. Chen, Z. Niu, M. Wang, T. Deng, Y. Qin, M. Han, X. Li
Nano-fiber based self-powered flexible vibration sensor for rail fasteners tightness safety detection
Nano Energy, 102 (2022), Article 107667, 10.1016/j.nanoen.2022.107667
View PDF View article View in Scopus Google Scholar

[52] 52
J. Qian, D.-S. Kim, D.-W. Lee
On-vehicle triboelectric nanogenerator enabled self-powered sensor for tire pressure monitoring
Nano Energy, 49 (2018), pp. 126-136, 10.1016/j.nanoen.2018.04.022
View PDF View article View in Scopus Google Scholar

[53] 53
Y. Feng, X. Huang, S. Liu, W. Guo, Y. Li, H. Wu
A self-powered smart safety belt enabled by triboelectric nanogenerators for driving status monitoring
Nano Energy, 62 (2019), pp. 197-204, 10.1016/j.nanoen.2019.05.043
View PDF View article View in Scopus Google Scholar

[54] 54
J.Y. Cho, K.-B. Kim, W.S. Hwang, C.H. Yang, J.H. Ahn, S.D. Hong, D.H. Jeon, G.J. Song, C.H. Ryu, S.B. Woo, et al.
A multifunctional road-compatible piezoelectric energy harvester for autonomous driver-assist LED indicators with a self-monitoring system
Appl. Energy, 242 (2019), pp. 294-301, 10.1016/j.apenergy.2019.03.075
View PDF View article View in Scopus Google Scholar

[55] 55
V.N. Thakur, J.I. Han
Triboelectric nanogenerator for smart traffic monitoring and safety
J. Ind. Eng. Chem., 124 (2023), pp. 89-101, 10.1016/j.jiec.2023.04.028
View PDF View article View in Scopus Google Scholar

[56] 56
X. Cao, Y. Xiong, J. Sun, X. Zhu, Q. Sun, Z.L. Wang
Piezoelectric Nanogenerators Derived Self-Powered Sensors for Multifunctional Applications and Artificial Intelligence
Adv. Funct. Mater., 31 (2021), 10.1002/adfm.202102983
Google Scholar

[57] 57
L. Farhan, O. Kaiwartya, L. Alzubaidi, W. Gheth, E. Dimla, R. Kharel
Toward Interference Aware IoT Framework: Energy and Geo-Location-Based-Modeling
IEEE Access, 7 (2019), pp. 56617-56630, 10.1109/ACCESS.2019.2913899
View in Scopus Google Scholar

[58] 58
F.K. Shaikh, S. Zeadally
Energy harvesting in wireless sensor networks: A comprehensive review
Renew. Sustain. Energy Rev., 55 (2016), pp. 1041-1054, 10.1016/j.rser.2015.11.010
View PDF View article View in Scopus Google Scholar

[59] 59
W. Balid, H.H. Refai
On the development of self-powered iot sensor for real-time traffic monitoring in smart cities
2017 IEEE SENSORS, IEEE (2017), pp. 1-3, 10.1109/ICSENS.2017.8234157
View in Scopus Google Scholar

[60] 60
H. Askari, E. Hashemi, A. Khajepour, M.B. Khamesee, Z.L. Wang
Tire Condition Monitoring and Intelligent Tires Using Nanogenerators Based on Piezoelectric, Electromagnetic, and Triboelectric Effects
Adv. Mater. Technol., 4 (2019), 10.1002/admt.201800105
Google Scholar

[61] 61
L. Liu, X. Guo, W. Liu, C. Lee
Recent Progress in the Energy Harvesting Technology—From Self-Powered Sensors to Self-Sustained IoT, and New Applications
Nanomaterials, 11 (2021), p. 2975, 10.3390/nano11112975
View in Scopus Google Scholar

[62] 62
V.J. Hodge, S. O’Keefe, M. Weeks, A. Moulds
Wireless Sensor Networks for Condition Monitoring in the Railway Industry: A Survey
IEEE Trans. Intell. Transport. Syst., 16 (2015), pp. 1088-1106, 10.1109/TITS.2014.2366512
View in Scopus Google Scholar

[63] 63
H. Alawad, S. Kaewunruen
Wireless Sensor Networks: Toward Smarter Railway Stations
Infrastructure, 3 (2018), p. 24, 10.3390/infrastructures3030024
View in Scopus Google Scholar

[64] 64
M.I.M. Ismail, R.A. Dziyauddin, R. Ahmad, N. Ahmad, N.A. Ahmad, A.M.A. Hamid
A Review of Energy Harvesting in Localization for Wireless Sensor Node Tracking
IEEE Access, 9 (2021), pp. 60108-60122, 10.1109/ACCESS.2021.3072061
View in Scopus Google Scholar

[65] 65
D. Milne, L. Le Pen, G. Watson, D. Thompson, W. Powrie, M. Hayward, S. Morley
Proving MEMS Technologies for Smarter Railway Infrastructure
Procedia Eng., 143 (2016), pp. 1077-1084, 10.1016/j.proeng.2016.06.222
View PDF View article View in Scopus Google Scholar

[66] 66
B. Dziadak, M. Kucharek, J. Starzyński
Powering the WSN Node for Monitoring Rail Car Parameters, Using a Piezoelectric Energy Harvester
Energies, 15 (2022), p. 1641, 10.3390/en15051641
View in Scopus Google Scholar

[67] 67
M. Huang, M. Zhu, X. Feng, Z. Zhang, T. Tang, X. Guo, T. Chen, H. Liu, L. Sun, C. Lee
Intelligent Cubic-Designed Piezoelectric Node (iCUPE) with Simultaneous Sensing and Energy Harvesting Ability toward Self-Sustained Artificial Intelligence of Things (AIoT)
ACS Nano, 17 (2023), pp. 6435-6451, 10.1021/acsnano.2c11366
View in Scopus Google Scholar

[68] 68
Y. Gong, S. Wang, Z. Xie, T. Zhang, W. Chen, X. Lu, Q. Zeng, Y. Gao, W. Huang
Self-Powered Wireless Sensor Node for Smart Railway Axle Box Bearing via a Variable Reluctance Energy Harvesting System
IEEE Trans. Instrum. Meas., 70 (2021), pp. 1-11, 10.1109/TIM.2021.3076857
View in Scopus Google Scholar

[69] 69
J. Lu, M. Gao, Y. Wang, P. Wang
Health monitoring of urban rail corrugation by wireless rechargeable sensor nodes
Struct. Health Monit., 18 (2019), pp. 838-852, 10.1177/1475921718782395
View in Scopus Google Scholar

[70] 70
M.V. Lopes, J.J. Eckert, T.S. Martins, A.A. Santos
Optimizing strain energy extraction from multi-beam piezoelectric devices for heavy haul freight cars
J. Braz. Soc. Mech. Sci. Eng., 42 (2020), p. 59, 10.1007/s40430-019-2150-8
Google Scholar

[71] 71
Y. Wu, N. Yang
Study of rail damage diagnosis and localization method based on intelligent wireless acoustic sensor network
Measurement, 217 (2023), Article 113032, 10.1016/j.measurement.2023.113032
View PDF View article View in Scopus Google Scholar

[72] 72
L. Wang, T. He, Z. Zhang, L. Zhao, C. Lee, G. Luo, Q. Mao, P. Yang, Q. Lin, X. Li, et al.
Self-sustained autonomous wireless sensing based on a hybridized TENG and PEG vibration mechanism
Nano Energy, 80 (2021), Article 105555, 10.1016/j.nanoen.2020.105555
View PDF View article View in Scopus Google Scholar

[73] 73
S. Cii, G. Tomasini, M.L. Bacci, D. Tarsitano
Solar Wireless Sensor Nodes for Condition Monitoring of Freight Trains
IEEE Trans. Intell. Transport. Syst., 23 (2022), pp. 3995-4007, 10.1109/TITS.2020.3038319
View in Scopus Google Scholar

[74] 74
Z. Kljaic, M. Cipek, T.J. Mlinaric, D. Pavkovic, D. Zorc
Utilization of Track Condition Information from Remote Wireless Sensor Network in Railways-A Mountainous Rail Track Case Study
2019 27th Telecommunications Forum (TELFOR), IEEE (2019), pp. 1-4, 10.1109/TELFOR48224.2019.8971066
Google Scholar

[75] 75
P. Li, Z. Long, Z. Yang
RF Energy Harvesting for Batteryless and Maintenance-Free Condition Monitoring of Railway Tracks
IEEE Internet Things J., 8 (2021), pp. 3512-3523, 10.1109/JIOT.2020.3023475
View in Scopus Google Scholar

[76] 76
Y. Bi, D. Feng, S. Liu, L. Jia, Y. Meng, W. Xu, J. Yang, S. Liu, B. Wang, Y. Qin, X. Li
Solid ion channels gel battery driven by triboelectric effect and its integrated self-powered foreign matter intrusion detecting system
Nano Energy, 83 (2021), Article 105791, 10.1016/j.nanoen.2021.105791
View PDF View article View in Scopus Google Scholar

[77] 77
E.D. Spyrou, T. Tsenis, V. Kappatos
Experimental Setup of integrated Wireless Accelerometer Network with LTE Gateway for a Rail Car Body Health Monitoring Application
2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), IEEE (2021), pp. 1-6, 10.1109/ICECCME52200.2021.9591140
Google Scholar

[78] 78
R. He, B. Ai, G. Wang, K. Guan, Z. Zhong, A.F. Molisch, C. Briso-Rodriguez, C.P. Oestges
High-Speed Railway Communications: From GSM-R to LTE-R
IEEE Veh. Technol. Mag., 11 (2016), pp. 49-58, 10.1109/MVT.2016.2564446
View in Scopus Google Scholar

[79] 79
R. He, B. Ai, Z. Zhong, M. Yang, R. Chen, J. Ding, Z. Ma, G. Sun, C. Liu
5G for Railways: Next Generation Railway Dedicated Communications
IEEE Commun. Mag., 60 (2022), pp. 130-136, 10.1109/MCOM.005.2200328
View in Scopus Google Scholar

[80] 80
J. Sramota, A. Skavhaug
RailCheck: A WSN-Based System for Condition Monitoring of Railway Infrastructure
2018 21st Euromicro Conference on Digital System Design (DSD), IEEE (2018), pp. 347-351, 10.1109/DSD.2018.00067
View in Scopus Google Scholar

[81] 81
Q. Quevy, G. Cornetta, A. Touhafi
A New Method for Acoustic Priority Vehicle Detection Based on a Self-Powering Triboelectric Acoustic Sensor Suitable for Low-Power Wireless Sensor Networks
Sensors, 21 (2020), p. 158, 10.3390/s21010158
Google Scholar

[82] 82
Q. Gao, B. Yang, J. Huo, J. Han
Embedded real-time and in-situ fatigue life monitoring sensor with load types identification
Sens Actuators A Phys, 347 (2022), Article 113945, 10.1016/j.sna.2022.113945
View PDF View article View in Scopus Google Scholar

[83] 83
N. Magaia, P. Gomes, L. Silva, B. Sousa, C.X. Mavromoustakis, G. Mastorakis
Development of Mobile IoT Solutions: Approaches, Architectures, and Methodologies
IEEE Internet Things J., 8 (2021), pp. 16452-16472, 10.1109/JIOT.2020.3046441
View in Scopus Google Scholar

[84] 84
T. Kamakshi, K. Pritika
Smart Railway Infrastructure: Fusion of IoT, RFID and VANET for Enhanced Safety and Operational Efficiency
2023 Second International Conference on Advances in Computational Intelligence and Communication (ICACIC), IEEE (2023), pp. 1-6, 10.1109/ICACIC59454.2023.10435249
Google Scholar

[85] 85
P. Fraga-Lamas, T.M. Fernández-Caramés, L. Castedo
Towards the Internet of Smart Trains: A Review on Industrial IoT-Connected Railways
Sensors, 17 (2017), p. 1457, 10.3390/s17061457
View in Scopus Google Scholar

[86] 86
Z. Wang, H. Du, W. Wang, Q. Zhang, F. Gu, A.D. Ball, C. Liu, X. Jiao, H. Qiu, D. Shi
A high performance contra-rotating energy harvester and its wireless sensing application toward green and maintain free vehicle monitoring
Appl. Energy, 356 (2024), Article 122370, 10.1016/j.apenergy.2023.122370
View PDF View article View in Scopus Google Scholar

[87] 87
X. Zhao, G. Wei, X. Li, Y. Qin, D. Xu, W. Tang, H. Yin, X. Wei, L. Jia
Self-powered triboelectric nano vibration accelerometer based wireless sensor system for railway state health monitoring
Nano Energy, 34 (2017), pp. 549-555, 10.1016/j.nanoen.2017.02.036
View PDF View article View in Scopus Google Scholar

[88] 88
M. Magno, F. Tombari, D. Brunelli, L. Di Stefano, L. Benini
Multimodal Video Analysis on Self-Powered Resource-Limited Wireless Smart Camera
IEEE J. Emerg. Sel. Top. Circuits Syst., 3 (2013), pp. 223-235, 10.1109/JETCAS.2013.2256833
View in Scopus Google Scholar

[89] 89
W. He, W. Shi, J. Le, H. Li, R. Ma
Geophone-Based Energy Harvesting Approach for Railway Wagon Monitoring Sensor With High Reliability and Simple Structure
IEEE Access, 8 (2020), pp. 35882-35891, 10.1109/ACCESS.2020.2968089
View in Scopus Google Scholar

[90] 90
W. Hwang, K.-B. Kim, J.Y. Cho, C.H. Yang, J.H. Kim, G.J. Song, Y. Song, D.H. Jeon, J.H. Ahn, S. Do Hong, et al.
Watts-level road-compatible piezoelectric energy harvester for a self-powered temperature monitoring system on an actual roadway
Appl. Energy, 243 (2019), pp. 313-320, 10.1016/j.apenergy.2019.03.122
View PDF View article View in Scopus Google Scholar

[91] 91
M. Liu, Y. Zhang, H. Fu, Y. Qin, A. Ding, E.M. Yeatman
A seesaw-inspired bistable energy harvester with adjustable potential wells for self-powered internet of train monitoring
Appl. Energy, 337 (2023), Article 120908, 10.1016/j.apenergy.2023.120908
View PDF View article View in Scopus Google Scholar

[92] 92
L. Jin, S.L. Zhang, S. Xu, H. Guo, W. Yang, Z.L. Wang
Free-Fixed Rotational Triboelectric Nanogenerator for Self-Powered Real-Time Wheel Monitoring
Adv. Mater. Technol., 6 (2021), Article 2000918, 10.1002/admt.202000918
View in Scopus Google Scholar

[93] 93
A.M. Tairab, H. Wang, D. Hao, A. Azam, A. Ahmed, Z. Zhang
A hybrid multimodal energy harvester for self-powered wireless sensors in the railway
Energy Sustain Dev., 68 (2022), pp. 150-169, 10.1016/j.esd.2022.03.012
View PDF View article View in Scopus Google Scholar

[94] 94
Y. Zhao, X. Yu, M. Chen, M. Zhang, Y. Chen, X. Niu, X. Sha, Z. Zhan, W.J. Li
Continuous Monitoring of Train Parameters Using IoT Sensor and Edge Computing
IEEE Sensor. J., 21 (2021), pp. 15458-15468, 10.1109/JSEN.2020.3026643
View in Scopus Google Scholar

[95] 95
M. Gao, Y. Li, J. Lu, Y. Wang, P. Wang, L. Wang
Condition monitoring of urban rail transit by local energy harvesting
Int. J. Distributed Sens. Netw., 14 (2018), Article 155014771881446, 10.1177/1550147718814469
Google Scholar

[96] 96
Z. Chen, Y. Gao, J. Liang
LOPdM: A Low-Power On-Device Predictive Maintenance System Based on Self-Powered Sensing and TinyML
IEEE Trans. Instrum. Meas., 72 (2023), pp. 1-13, 10.1109/TIM.2023.3308251
Google Scholar

[97] 97
R. Li, D. Wei, Z. Wang
Synergizing Machine Learning Algorithm with Triboelectric Nanogenerators for Advanced Self-Powered Sensing Systems
Nanomaterials, 14 (2024), p. 165, 10.3390/nano14020165
View in Scopus Google Scholar

[98] 98
V. Fragnelli, S. Sanguineti
A game theoretic model for re-optimizing a railway timetable
Eur. Transp. Res. Rev., 6 (2014), pp. 113-125, 10.1007/s12544-013-0116-y
View in Scopus Google Scholar

[99] 99
M. Yin, K. Li, X. Cheng
A review on artificial intelligence in high-speed rail
Transportation Safety and Environment, 2 (2020), pp. 247-259, 10.1093/tse/tdaa022
View in Scopus Google Scholar

[100] 100
L. Zhu, C. Chen, H. Wang, F.R. Yu, T. Tang
Machine Learning in Urban Rail Transit Systems: A Survey
IEEE Trans. Intell. Transport. Syst., 25 (2024), pp. 2182-2207, 10.1109/TITS.2023.3319135
View in Scopus Google Scholar

[101] 101
J. Alzubi, A. Nayyar, A. Kumar
Machine Learning from Theory to Algorithms: An Overview
Journal of Physics: Conference Series, 1142, IOP Publishing (2018), Article 012012, 10.1088/1742-6596/1142/1/012012
View in Scopus Google Scholar

[102] 102
P. Singh, M.A. Dulebenets, J. Pasha, E.D.R.S. Gonzalez, Y.-Y. Lau, R. Kampmann
Deployment of Autonomous Trains in Rail Transportation: Current Trends and Existing Challenges
IEEE Access, 9 (2021), pp. 91427-91461, 10.1109/ACCESS.2021.3091550
View in Scopus Google Scholar

[103] 103
J. Leukel, J. González, M. Riekert
Adoption of machine learning technology for failure prediction in industrial maintenance: A systematic review
J. Manuf. Syst., 61 (2021), pp. 87-96, 10.1016/j.jmsy.2021.08.012
View PDF View article View in Scopus Google Scholar

[104] 104
M. Mićić, L. Brajović, L. Lazarević, Z. Popović
Inspection of RCF rail defects – Review of NDT methods
Mech. Syst. Signal Process., 182 (2023), Article 109568, 10.1016/j.ymssp.2022.109568
View PDF View article View in Scopus Google Scholar

[105] 105
A. Peinado Gonzalo, M. Entezami, P. Weston, C. Roberts, F. Pedro Garcia Marquez
Railway Track and Vehicle Onboard Monitoring: A Review
E3S Web Conf., 409 (2023), Article 02014, 10.1051/e3sconf/202340902014
View in Scopus Google Scholar

[106] 106
J.M. Castillo-Mingorance, M. Sol-Sánchez, F. Moreno-Navarro, M.C. Rubio-Gámez
A Critical Review of Sensors for the Continuous Monitoring of Smart and Sustainable Railway Infrastructures
Sustainability, 12 (2020), p. 9428, 10.3390/su12229428
Google Scholar

[107] 107
A.H. Alavi, H. Hasni, N. Lajnef, K. Chatti
Continuous health monitoring of pavement systems using smart sensing technology
Construct. Build. Mater., 114 (2016), pp. 719-736, 10.1016/j.conbuildmat.2016.03.128
View PDF View article View in Scopus Google Scholar

[108] 108
C. Chellaswamy, L. Balaji, A. Vanathi, L. Saravanan
IoT based rail track health monitoring and information system
2017 International conference on Microelectronic Devices, Circuits and Systems (ICMDCS), IEEE (2017), pp. 1-6, 10.1109/ICMDCS.2017.8211548
Google Scholar

[109] 109
Y. Sun, Y. Wang, S. Tian, S. Huo, M. Hu, F. Wu, Q. Yi, B. An, P. Wang, M. Gao
Energy Self-Sufficient Rail Corrugation Identification by a Multistable Piezo-Electro-Magnet Coupled Energy Transducer
IEEE Trans. Instrum. Meas., 72 (2023), pp. 1-13, 10.1109/TIM.2023.3309382
Google Scholar

[110] 110
Y. Sun, P. Wang, J. Lu, J. Xu, P. Wang, S. Xie, Y. Li, J. Dai, B. Wang, M. Gao
Rail corrugation inspection by a self-contained triple-repellent electromagnetic energy harvesting system
Appl. Energy, 286 (2021), Article 116512, 10.1016/j.apenergy.2021.116512
View PDF View article View in Scopus Google Scholar

[111] 111
T. Wang, Y. He, B. Li, T. Shi
Transformer Fault Diagnosis Using Self-Powered RFID Sensor and Deep Learning Approach
IEEE Sensor. J., 18 (2018), pp. 6399-6411, 10.1109/JSEN.2018.2844799
View in Scopus Google Scholar

[112] 112
H. Zhong, L. Liu, J. Wang, Q. Fu, B. Yi
A real-time railway fastener inspection method using the lightweight depth estimation network
Measurement, 189 (2022), Article 110613, 10.1016/j.measurement.2021.110613
View PDF View article View in Scopus Google Scholar

[113] 113
J. Xu, X. Wei, R. Li, S. Kong, Z. Wu, Z.L. Wang
A Capsule-Shaped Triboelectric Nanogenerator for Self-Powered Health Monitoring of Traffic Facilities
ACS Mater. Lett., 4 (2022), pp. 1630-1637, 10.1021/acsmaterialslett.2c00477
View in Scopus Google Scholar

[114] 114
C. Tarawneh, J.A. Aranda, V.V. Hernandez, C.J. Ramirez
An Analysis of the Efficacy of Wayside Hot-Box Detector Data
2018 Joint Rail Conference, American Society of Mechanical Engineers (2018), 10.1115/JRC2018-6218
Google Scholar

[115] 115
A.H. Alavi, H. Hasni, P. Jiao, W. Borchani, N. Lajnef
Fatigue cracking detection in steel bridge girders through a self-powered sensing concept
J. Constr. Steel Res., 128 (2017), pp. 19-38, 10.1016/j.jcsr.2016.08.002
View PDF View article View in Scopus Google Scholar

[116] 116
Z. Li, B. Cai, J. Liu, D. Lu
Modelling and performance analysis of Balise under dynamic energy harvesting in high-speed railway
IET Intell. Transp. Syst., 16 (2022), pp. 1504-1520, 10.1049/itr2.12228
View in Scopus Google Scholar

[117] 117
X. Lu, H. Li, X. Zhang, B. Gao, T. Cheng
Magnetic-assisted self-powered acceleration sensor for real-time monitoring vehicle operation and collision based on triboelectric nanogenerator
Nano Energy, 96 (2022), Article 107094, 10.1016/j.nanoen.2022.107094
View PDF View article View in Scopus Google Scholar

[118] 118
S. Kinden, Z. Batmaz
Vehicle Classification Using Deep Learning-Assisted Triboelectric Sensor
Arabian J. Sci. Eng., 49 (2023), pp. 6657-6673, 10.1007/s13369-023-08394-4
Google Scholar

[119] 119
H. Zhang, Y. Zhou
AI-based modeling and data-driven identification of moving load on continuous beams
Fundam. Res., 3 (2023), pp. 796-803, 10.1016/j.fmre.2022.02.013
View PDF View article View in Scopus Google Scholar

[120] 120
E. Bernal, M. Spiryagin, C. Cole
Onboard Condition Monitoring Sensors, Systems and Techniques for Freight Railway Vehicles: A Review
IEEE Sensor. J., 19 (2019), pp. 4-24, 10.1109/JSEN.2018.2875160
View in Scopus Google Scholar

[121] 121
O. Brignole, C. Cavalletti, A. Maresca, N. Mazzino, M. Balato, A. Buonomo, L. Costanzo, M. Giorgio, R. Langella, A.L. Schiavo, et al.
Resonant electromagnetic vibration harvesters feeding sensor nodes for real-time diagnostics and monitoring in railway vehicles for goods transportation: A numerical-experimental analysis
2016 IEEE International Power Electronics and Motion Control Conference (PEMC), IEEE (2016), pp. 456-461, 10.1109/EPEPEMC.2016.7752040
View in Scopus Google Scholar

[122] 122
D. Huang, S. Li, N. Qin, Y. Zhang
Fault Diagnosis of High-Speed Train Bogie Based on the Improved-CEEMDAN and 1-D CNN Algorithms
IEEE Trans. Instrum. Meas., 70 (2021), pp. 1-11, 10.1109/TIM.2020.3047922
View in Scopus Google Scholar

[123] 123
Z. Fang, Z. Zhou, M. Yi, Z. Zhang, X. Luo, A. Ahmed
A roller-bearing-based triboelectric nanosensor for freight train synergistic maintenance in smart transportation
Nano Energy, 106 (2023), Article 108089, 10.1016/j.nanoen.2022.108089
View PDF View article View in Scopus Google Scholar

[124] 124
Q. Zheng, Y. Hou, H. Yang, P. Tan, H. Shi, Z. Xu, Z. Ye, N. Chen, X. Qu, X. Han, et al.
Towards a sustainable monitoring: A self-powered smart transportation infrastructure skin
Nano Energy, 98 (2022), Article 107245, 10.1016/j.nanoen.2022.107245
View PDF View article View in Scopus Google Scholar

[125] 125
J. Yang, Y. Sun, J. Zhang, B. Chen, Z.L. Wang
3D-printed bearing structural triboelectric nanogenerator for intelligent vehicle monitoring
Cell Rep. Phys. Sci., 2 (2021), Article 100666, 10.1016/j.xcrp.2021.100666
View PDF View article View in Scopus Google Scholar

[126] 126
H. Askari, Z. Saadatnia, A. Khajepour, M.B. Khamesee, J. Zu
A Triboelectric Self-Powered Sensor for Tire Condition Monitoring: Concept, Design, Fabrication, and Experiments
Adv. Eng. Mater., 19 (2017), Article 1700318, 10.1002/adem.201700318
View in Scopus Google Scholar

[127] 127
E. Bernal, M. Spiryagin, C. Cole
Ultra-Low Power Sensor Node for On-Board Railway Wagon Monitoring
IEEE Sensor. J., 20 (2020), pp. 15185-15192, 10.1109/JSEN.2020.3011132
View in Scopus Google Scholar

[128] 128
S.K. Lai, C. Wang, L.H. Zhang, Y.Q. Ni
Realizing a Self-powered Real-time Monitoring System on High-speed Trains
Internoise, 263 (2021), pp. 434-441, 10.3397/IN-2021-1476
Google Scholar

[129] 129
M. Jiang, B. Li, W. Jia, Z. Zhu
Predicting output performance of triboelectric nanogenerators using deep learning model
Nano Energy, 93 (2022), Article 106830, 10.1016/j.nanoen.2021.106830
View PDF View article View in Scopus Google Scholar

[130] 130
J. Wang, Z. Shi, H. Xiang, G. Song
Modeling on energy harvesting from a railway system using piezoelectric transducers
Smart Mater. Struct., 24 (2015), Article 105017, 10.1088/0964-1726/24/10/105017
View in Scopus Google Scholar

[131] 131
L. Tian, Y. Huang, S. Liu, S. Sun, J. Deng, H. Zhao
Application of photovoltaic power generation in rail transit power supply system under the background of energy low carbon transformation
Alex. Eng. J., 60 (2021), pp. 5167-5174, 10.1016/j.aej.2021.04.008
View PDF View article View in Scopus Google Scholar

[132] 132
Y. Pang, X. Zhu, Y. Yu, S. Liu, Y. Chen, Y. Feng
Waterbomb-origami inspired triboelectric nanogenerator for smart pavement-integrated traffic monitoring
Nano Res., 15 (2022), pp. 5450-5460, 10.1007/s12274-022-4152-6
View in Scopus Google Scholar

[133] 133
D. Zhu, X. Guo, H. Li, Z. Yuan, X. Zhang, T. Cheng
Self-powered flow sensing for automobile based on triboelectric nanogenerator with magnetic field modulation mechanism
Nano Energy, 108 (2023), Article 108233, 10.1016/j.nanoen.2023.108233
View PDF View article View in Scopus Google Scholar

[134] 134
H. Behrooz, Y.M. Hayeri
Machine Learning Applications in Surface Transportation Systems: A Literature Review
Appl. Sci., 12 (2022), p. 9156, 10.3390/app12189156
View in Scopus Google Scholar

[135] 135
A.F. Villán
Facial attributes recognition using computer vision to detect drowsiness and distraction in drivers
ELCVIA Electron. Lett. Comput. Vis. Image Anal., 16 (2017), pp. 25-28, 10.5565/rev/elcvia.1134
Google Scholar

[136] 136
S. Fu, Z. Yang, Y. Ma, Z. Li, L. Xu, H. Zhou
Advancements in the Intelligent Detection of Driver Fatigue and Distraction: A Comprehensive Review
Appl. Sci., 14 (2024), p. 3016, 10.3390/app14073016
View in Scopus Google Scholar

[137] 137
H. Li, M. Liang, K. Niu, Y. Zhang
A Human-Machine Trust Evaluation Method for High-Speed Train Drivers Based on Multi-Modal Physiological Information
Int. J. Hum. Comput. Interact., 0 (2024), pp. 1-18, 10.1080/10447318.2024.2327188
Google Scholar

[138] 138
X. Lu, H. Zhang, X. Zhao, H. Yang, L. Zheng, W. Wang, C. Sun
Triboelectric nanogenerator based self-powered sensor with a turnable sector structure for monitoring driving behavior
Nano Energy, 89 (2021), Article 106352, 10.1016/j.nanoen.2021.106352
View PDF View article View in Scopus Google Scholar

[139] 139
L. Chen, K. Yuan, S. Chen, Y. Huang, H. Askari, N. Yu, J. Mo, N. Xu, M. Wu, H. Chen, et al.
Triboelectric nanogenerator sensors for intelligent steering wheel aiming at automated driving
Nano Energy, 113 (2023), Article 108575, 10.1016/j.nanoen.2023.108575
View PDF View article View in Scopus Google Scholar

[140] 140
Z. Xie, Z. Zeng, Y. Wang, W. Yang, Y. Xu, X. Lu, T. Cheng, H. Zhao, Z.L. Wang
Novel sweep-type triboelectric nanogenerator utilizing single freewheel for random triggering motion energy harvesting and driver habits monitoring
Nano Energy, 68 (2020), Article 104360, 10.1016/j.nanoen.2019.104360
View PDF View article View in Scopus Google Scholar

[141] 141
X. Lu, B. Leng, H. Li, X. Lv, X. Zhang, T. Qu, S. Li, Y. Wang, J. Wen, B. Zhang, T. Cheng
An Intelligent Driving Monitoring System Utilizing Pedal Motion Sensor Integrated with Triboelectric-Electromagnetic Hybrid Generator and Machine Learning
Adv. Mater. Technol., 9 (2024), Article 2301706, 10.1002/admt.202301706
View in Scopus Google Scholar

[142] 142
X. Zhang, Z. Yang, S. Yang, X. Zhang, H. Li, X. Lu, B. Zhang, Z.L. Wang, T. Cheng
Deep learning assisted three triboelectric driving operation sensors for driver training and behavior monitoring
Mater. Today, 72 (2024), pp. 47-56, 10.1016/j.mattod.2023.11.007
View PDF View article Google Scholar

[143] 143
F. Luo, B. Chen, X. Ran, W. Ouyang, Y. Yao, L. Shang
Wearable and self-powered triboelectric sensors based on NaCl/PVA hydrogel for driver multidimensional information monitoring
Nano Energy, 118 (2023), Article 109035, 10.1016/j.nanoen.2023.109035
View PDF View article View in Scopus Google Scholar

[144] 144
J. Chen, L. Kong, Z. Fang, R. Zou, J. Wu, H. Tang, Z. Zhang
A self-powered and self-sensing driver behavior detection system for smart transportation
Nano Energy, 122 (2024), Article 109327, 10.1016/j.nanoen.2024.109327
View PDF View article Google Scholar

[145] 145
A. S Aghaei, B. Donmez, C.C. Liu, D. He, G. Liu, K.N. Plataniotis, H.-Y.W. Chen, Z. Sojoudi
Smart Driver Monitoring: When Signal Processing Meets Human Factors: In the driver’s seat
IEEE Signal Process. Mag., 33 (2016), pp. 35-48, 10.1109/MSP.2016.2602379
Google Scholar

[146] 146
J. Yang, S. Liu, Y. Meng, W. Xu, S. Liu, L. Jia, G. Chen, Y. Qin, M. Han, X. Li
Self-Powered Tactile Sensor for Gesture Recognition Using Deep Learning Algorithms
ACS Appl. Mater. Interfaces, 14 (2022), pp. 25629-25637, 10.1021/acsami.2c01730
View in Scopus Google Scholar

[147] 147
H. Zhang, H. Tan, W. Wang, Z. Li, F. Chen, X. Jiang, X. Lu, Y. Hu, L. Li, J. Zhang, et al.
Real-Time Non-Driving Behavior Recognition Using Deep Learning-Assisted Triboelectric Sensors in Conditionally Automated Driving
Adv. Funct. Mater., 33 (2023), Article 2210580, 10.1002/adfm.202210580
View in Scopus Google Scholar

[148] 148
L. Dong, H. Zhang, J. Yu, G. Hu
Energy harvesting potential assessment and systematic design for energy-regenerative shock absorbers on railway freight wagons
J. Intell. Mater. Syst. Struct., 35 (2024), pp. 270-290, 10.1177/1045389X231200146
View in Scopus Google Scholar

[149] 149
H. Fu, W. Song, Y. Qin, E.M. Yeatman
Broadband Vibration Energy Harvesting from Underground Trains for Self-Powered Condition Monitoring
2019 19th International Conference on Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS), IEEE (2019), pp. 1-5, 10.1109/PowerMEMS49317.2019.93285904627
View in Scopus Google Scholar

[150] 150
J. Zuo, L. Dong, J. Ding, X. Wang, P. Diao, J. Yu
Design and validation of a self-powered device for wireless electronically controlled pneumatic brake and onboard monitoring in freight wagons
Energy Convers. Manag., 239 (2021), Article 114229, 10.1016/j.enconman.2021.114229
View PDF View article View in Scopus Google Scholar

[151] 151
T. Guo, J. Zhao, W. Liu, G. Liu, Y. Pang, T. Bu, F. Xi, C. Zhang, X. Li
Self-Powered Hall Vehicle Sensors Based on Triboelectric Nanogenerators
Adv. Mater. Technol., 3 (2018), Article 1800140, 10.1002/admt.201800140
View in Scopus Google Scholar

[152] 152
J. Lian, X. Ding, D. Cai, M. Yue
Intelligent adaptive control method for key parameters of vibration rolling during high-speed railway subgrade compaction
Transp. Geotech., 41 (2023), Article 101000, 10.1016/j.trgeo.2023.101000
View PDF View article View in Scopus Google Scholar

[153] 153
Y. Sun, H. Qiang, J. Xu, G. Lin
Internet of Things-Based Online Condition Monitor and Improved Adaptive Fuzzy Control for a Medium-Low-Speed Maglev Train System
IEEE Trans. Ind. Inf., 16 (2020), pp. 2629-2639, 10.1109/TII.2019.2938145
View in Scopus Google Scholar

[154] 154
P. Wang, T. Mei, J. Zhang, H. Li
Self-Powered Active Lateral Secondary Suspension for Railway Vehicles
IEEE Trans. Veh. Technol., 65 (2016), pp. 1121-1129, 10.1109/TVT.2015.2407575
View in Scopus Google Scholar

[155] 155
P. Wang, H. Li, J. Zhang, T. Mei
An analytical design approach for self-powered active lateral secondary suspensions for railway vehicles
Veh. Syst. Dyn., 53 (2015), pp. 1439-1454, 10.1080/00423114.2015.1050964
View in Scopus Google Scholar

[156] 156
V.A. Morais, J.L. Afonso, A.P. Martins
Towards Smart Railways: A Charging Strategy for On-Board Energy Storage Systems
P.J.G. Afonso, L. João, Monteiro (Eds.), Sustainable Energy for Smart Cities, Springer International Publishing (2020), pp. 33-46, 10.1007/978-3-030-45694-8_3
View in Scopus Google Scholar

[157] 157
Y. Fan, L. Zhang, D. Li, Z. Wang
Progress in self-powered, multi-parameter, micro sensor technologies for power metaverse and smart grids
Nano Energy, 118 (2023), Article 108959, 10.1016/j.nanoen.2023.108959
View PDF View article View in Scopus Google Scholar

[158] 158
Y. Liao, W. Li, K. Wang, J. Guo, Y. Shen, Q. Wang, Y. Zhang, C. Wu, X. Zhou, T. Guo, T.W. Kim
TENG-inspired LED-in-capacitors for smart self-powered high-voltage monitoring and high-sensitivity demodulation of power-line communications
Nano Energy, 102 (2022), Article 107698, 10.1016/j.nanoen.2022.107698
View PDF View article View in Scopus Google Scholar

[159] 159
A. Hussain, V.-H. Bui, H.-M. Kim
Microgrids as a resilience resource and strategies used by microgrids for enhancing resilience
Appl. Energy, 240 (2019), pp. 56-72, 10.1016/j.apenergy.2019.02.055
View PDF View article View in Scopus Google Scholar

[160] 160
R.S. Peres, X. Jia, J. Lee, K. Sun, A.W. Colombo, J. Barata
Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook
IEEE Access, 8 (2020), pp. 220121-220139, 10.1109/ACCESS.2020.3042874
View in Scopus Google Scholar

[161] 161
Y. Tang, X. Dai, C. Zhao, Q. Cheng, Y. Lv
Large Language Model-Driven Urban Traffic Signal Control
2024 Australian & New Zealand Control Conference (ANZCC), IEEE (2024), pp. 67-71, 10.1109/ANZCC59813.2024.10432823
View in Scopus Google Scholar

[162] 162
H. Wang, Y.-F. Li
Large Language Model Empowered by Domain-Specific Knowledge Base for Industrial Equipment Operation and Maintenance
2023 5th International Conference on System Reliability and Safety Engineering (SRSE), IEEE (2023), pp. 474-479, 10.1109/SRSE59585.2023.10336112
View in Scopus Google Scholar

[163] 163
H. Wang, Y.-F. Li
Large-Scale Language Models for PHM in Railway Systems - Potential Applications, Limitations, and Solutions
J. Yang, D. Yao, L. Jia, Y. Qin, Z. Liu, L. Diao (Eds.), Proceedings of the 6th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2023, Springer Nature Singapore (2024), pp. 591-599, 10.1007/978-981-99-9311-6_59
Google Scholar

[164] 164
F. Tao, H. Zhang, A. Liu, A.Y.C. Nee
Digital Twin in Industry: State-of-the-Art
IEEE Trans. Ind. Inf., 15 (2019), pp. 2405-2415, 10.1109/TII.2018.2873186
View in Scopus Google Scholar

[165] 165
L. Zhao, G. Han, Z. Li, L. Shu
Intelligent Digital Twin-Based Software-Defined Vehicular Networks
IEEE Netw., 34 (2020), pp. 178-184, 10.1109/MNET.011.1900587
View in Scopus Google Scholar

[166] 166
Z. Tang, L. Ling, T. Zhang, Y. Hu, K. Wang, W. Zhai
Towards digital twin trains: implementing a cloud-based framework for railway vehicle dynamics simulation
Int. J. Real. Ther., 0 (2024), pp. 1-24, 10.1080/23248378.2024.2355578
Google Scholar

[167] 167
S. Ahmad, M. Spiryagin, Q. Wu, E. Bernal, Y. Sun, C. Cole, B. Makin
Development of a Digital Twin for prediction of rail surface damage in heavy haul railway operations
Veh. Syst. Dyn., 62 (2024), pp. 41-66, 10.1080/00423114.2023.2237620
Google Scholar

[168] 168
Y. Yu, L. Guo, K. Deng, H. Gao, B. Wang
Enabling Safety Guarantees of Urban Rail Transit: A New Digital Twin Framework for Data-Model Driven Track Condition Diagnosis
J. Yang, D. Yao, L. Jia, Y. Qin, Z. Liu, L. Diao (Eds.), Proceedings of the 6th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2023, Springer Nature Singapore (2024), pp. 629-637, 10.1007/978-981-99-9319-2_70
View in Scopus Google Scholar

[169] 169
C. Liu, D. He, Z. Wei, C. He, Z. Lao, S. Shan
Personalized fault diagnosis of rolling bearings in trains based on digital twin
Meas. Sci. Technol., 34 (2023), Article 125131, 10.1088/1361-6501/acf517
View in Scopus Google Scholar

[170] 170
X. Wu, W. Lian, M. Zhou, H. Song, H. Dong
A Digital Twin-Based Fault Diagnosis Framework for Bogies of High-Speed Trains
IEEE J. Radio Freq. Identif., 7 (2023), pp. 203-207, 10.1109/JRFID.2022.3216331
View in Scopus Google Scholar

[171] 171
N. Bosso, M. Magelli, R. Trinchero, N. Zampieri
Application of machine learning techniques to build digital twins for long train dynamics simulations
Veh. Syst. Dyn., 62 (2024), pp. 21-40, 10.1080/00423114.2023.2174885
Google Scholar

[172] 172
Y. Wang, G. Zhang, R. Chen, Z. Liu, R. Qiu
Analysis of digital twin application of urban rail power supply system for energy saving
Proceedings 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence, DTPI 2021, Institute of Electrical and Electronics Engineers Inc (2021), pp. 29-32, 10.1109/DTPI52967.2021.9540127
Google Scholar

[173] 173
A. Bussmann-Holder, H. Keller
High-temperature superconductors: underlying physics and applications
Z. Naturforsch. B Chem. Sci., 75 (2020), pp. 3-14, 10.1515/znb-2019-0103
Google Scholar

[174] 174
A. Allais, J.-M. Saugrain, B. West, N. Lallouet, H. Caron, D. Ferandelle, L. Terrien, G. Bouvier, G. Hajiri, K. Berger, L. Quéval
SuperRail–World-First HTS Cable to be Installed on a Railway Network in France
IEEE Trans. Appl. Supercond., 34 (2024), pp. 1-7, 10.1109/TASC.2024.3356450
View in Scopus Google Scholar

[175] 175
L. Wang, Z. Deng, H. Wang, H. Li, K. Li, S. Ma
Dynamic Responses of HTS Maglev System Under Track Random Irregularity
IEEE Trans. Appl. Supercond., 30 (2020), pp. 1-7, 10.1109/TASC.2020.2976995
Google Scholar

[176] 176
Y. Fu, H. Li, L. Wang, P. Zhang, K. Zhang, Z. Deng
Dynamic simulation of HTS maglev vehicle/track coupled based on discrete elastic support track model
Physica C (Amsterdam, Neth.), 592 (2022), Article 1353997, 10.1016/j.physc.2021.1353997
View PDF View article View in Scopus Google Scholar

[177] 177
Q. Li, Z. Deng, L. Wang, H. Li, J. Zhang, E.F. Rodriguez
Active vibration control of secondary suspension based on high-temperature superconducting maglev vehicle system
Physica C (Amsterdam, Neth.), 585 (2021), Article 1353872, 10.1016/j.physc.2021.1353872
View PDF View article View in Scopus Google Scholar

[178] 178
K.M. Hou, X. Diao, H. Shi, H. Ding, H. Zhou, C. de Vaulx
Trends and Challenges in AIoT/IIoT/IoT Implementation
Sensors, 23 (2023), p. 5074, 10.3390/s23115074
View in Scopus Google Scholar

[179] 179
R. De Fazio, M. De Giorgi, D. Cafagna, C. Del-Valle-Soto, P. Visconti
Energy Harvesting Technologies and Devices from Vehicular Transit and Natural Sources on Roads for a Sustainable Transport: State-of-the-Art Analysis and Commercial Solutions
Energies, 16 (2023), p. 3016, 10.3390/en16073016
View in Scopus Google Scholar

[180] 180
H. Ryu, H.J. Yoon, S.W. Kim
Hybrid Energy Harvesters: Toward Sustainable Energy Harvesting
Adv. Mater., 31 (2019), p. e1802898, 10.1002/adma.201802898
Google Scholar

[181] 181
R. Bokade, A. Navato, R. Ouyang, X. Jin, C.-A. Chou, S. Ostadabbas, A.V. Mueller
A cross-disciplinary comparison of multimodal data fusion approaches and applications: Accelerating learning through trans-disciplinary information sharing
Expert Syst. Appl., 165 (2021), Article 113885, 10.1016/j.eswa.2020.113885
View PDF View article View in Scopus Google Scholar

[182] 182
Q. Shi, B. Dong, T. He, Z. Sun, J. Zhu, Z. Zhang, C. Lee
Progress in wearable electronics/photonics—Moving toward the era of artificial intelligence and internet of things
InfoMat, 2 (2020), pp. 1131-1162, 10.1002/inf2.12122
View in Scopus Google Scholar

[183] 183
X. Yang, Z. Luo, Z. Huang, Y. Zhao, Z. Xue, Y. Wang, W. Liu, S. Liu, H. Zhang, K. Xu, et al.
Development Status and Prospects of Artificial Intelligence in the Field of Energy Conversion Materials
Front. Energy Res., 8 (2020), p. 167, 10.3389/fenrg.2020.00167
Google Scholar

Outline 概要

Figures (15) 数字 (15)

Tables (3)

iScience

Summary 摘要

Graphical abstract 图形摘要

Subject areas 学科领域

Introduction 导言

Self-powered sensing technology
自供电传感技术

Advanced IOT applications
先进的物联网应用

Wireless smart sensors (WSS)
无线智能传感器（WSS）

Wireless sensor network (WSN)
无线传感器网络（WSN）

IoT platform 物联网平台

Advanced algorithms 高级算法

Anomaly detection for rail environment
铁路环境异常检测

State detection of rail vehicles
轨道车辆的状态检测

Behavior analysis of train drivers
火车司机行为分析

Optimization and control of rail transit devices
轨道交通设备的优化与控制

Energy management of rail transit devices
轨道交通设备的能源管理

Opportunities for emerging technologies
新兴技术的机遇

Large language models (LLMs) for human-machine interaction
用于人机交互的大型语言模型 (LLMs)

Digital twin (DT) for the virtual
虚拟数字孪生（DT）

High-temperature superconducting (HTS) and magnetic levitation (maglev) for high-speed railways
用于高速铁路的高温超导（HTS）和磁悬浮（maglev）

Conclusion and challenges
结论与挑战

Multifunctional deployment integration
多功能部署集成

Customization and improvement of technologies and algorithms
定制和改进技术与算法

Cross-disciplinary 跨学科

Acknowledgments 致谢

Author contributions 作者供稿

Declaration of interests 利益声明

References

Cited by (0)

iScience

Review 评论Sustainable and smart rail transit based on advanced self-powered sensing technology基于先进自供电传感技术的可持续智能轨道交通

Summary 摘要

Graphical abstract 图形摘要

Subject areas 学科领域

Introduction 导言

Self-powered sensing technology自供电传感技术

Advanced IOT applications先进的物联网应用

Wireless smart sensors (WSS)无线智能传感器（WSS）

Wireless sensor network (WSN)无线传感器网络（WSN）

IoT platform 物联网平台

Advanced algorithms 高级算法

Anomaly detection for rail environment铁路环境异常检测

State detection of rail vehicles轨道车辆的状态检测

Behavior analysis of train drivers火车司机行为分析

Optimization and control of rail transit devices轨道交通设备的优化与控制

Energy management of rail transit devices轨道交通设备的能源管理

Opportunities for emerging technologies新兴技术的机遇

Large language models (LLMs) for human-machine interaction用于人机交互的大型语言模型 (LLMs)

Digital twin (DT) for the virtual虚拟数字孪生（DT）

High-temperature superconducting (HTS) and magnetic levitation (maglev) for high-speed railways用于高速铁路的高温超导（HTS）和磁悬浮（maglev）

Conclusion and challenges结论与挑战

Multifunctional deployment integration多功能部署集成

Customization and improvement of technologies and algorithms定制和改进技术与算法

Cross-disciplinary 跨学科

Acknowledgments 致谢

Author contributions 作者供稿

Declaration of interests 利益声明

References

Review 评论
Sustainable and smart rail transit based on advanced self-powered sensing technology
基于先进自供电传感技术的可持续智能轨道交通

Self-powered sensing technology
自供电传感技术

Advanced IOT applications
先进的物联网应用

Wireless smart sensors (WSS)
无线智能传感器（WSS）

Wireless sensor network (WSN)
无线传感器网络（WSN）

Anomaly detection for rail environment
铁路环境异常检测

State detection of rail vehicles
轨道车辆的状态检测

Behavior analysis of train drivers
火车司机行为分析

Optimization and control of rail transit devices
轨道交通设备的优化与控制

Energy management of rail transit devices
轨道交通设备的能源管理

Opportunities for emerging technologies
新兴技术的机遇

Large language models (LLMs) for human-machine interaction
用于人机交互的大型语言模型 (LLMs)

Digital twin (DT) for the virtual
虚拟数字孪生（DT）

High-temperature superconducting (HTS) and magnetic levitation (maglev) for high-speed railways
用于高速铁路的高温超导（HTS）和磁悬浮（maglev）

Conclusion and challenges
结论与挑战

Multifunctional deployment integration
多功能部署集成

Customization and improvement of technologies and algorithms
定制和改进技术与算法