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Condition monitoring of urban rail transit by local energy harvesting

Author

Listed:
  • Mingyuan Gao
  • Yunwu Li
  • Jun Lu
  • Yifeng Wang
  • Ping Wang
  • Li Wang

Abstract

The goal of this study is to develop a vibration-based electromagnetic energy harvesting prototype that provides power to rail-side monitoring equipment and sensors by collecting wheel-rail vibration energy when the train travels. This technology helps power rail–side equipment in off-grid and remote areas. This article introduces the principle, modeling, and experimental test of the system, including (1) an electromagnetic energy harvesting prototype with DC-DC boost converter and lithium battery charge management function, (2) wireless sensor nodes integrated with accelerometer and temperature/humidity sensor, and (3) a vehicle-track interaction model that considers wheel out-of-roundness. Field test results, power consumption, Littlewood–Paley wavelet transform method, and feasibility analysis are reported. An application case of the technology is introduced: the sensor nodes of the wireless sensor network are powered by the electromagnetic energy harvester and lithium battery with DC-DC boost converter, thereby continuously monitoring the railway track state; based on the Littlewood–Paley wavelet analysis of measured railway track acceleration data, the abnormal signal caused by the wheel out-of-roundness can be detected.

Suggested Citation

  • Mingyuan Gao & Yunwu Li & Jun Lu & Yifeng Wang & Ping Wang & Li Wang, 2018. "Condition monitoring of urban rail transit by local energy harvesting," International Journal of Distributed Sensor Networks, , vol. 14(11), pages 15501477188, November.
    • Handle: RePEc:sae:intdis:v:14:y:2018:i:11:p:1550147718814469
    DOI: 10.1177/1550147718814469
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    References listed on IDEAS

    as
    1. Gao, Mingyuan & Wang, Yuan & Wang, Yifeng & Wang, Ping, 2018. "Experimental investigation of non-linear multi-stable electromagnetic-induction energy harvesting mechanism by magnetic levitation oscillation," Applied Energy, Elsevier, vol. 220(C), pages 856-875.
    2. Zhou, Shengxi & Cao, Junyi & Inman, Daniel J. & Lin, Jing & Liu, Shengsheng & Wang, Zezhou, 2014. "Broadband tristable energy harvester: Modeling and experiment verification," Applied Energy, Elsevier, vol. 133(C), pages 33-39.
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    Citations

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    Cited by:

    1. Pan, Hongye & Qi, Lingfei & Zhang, Zutao & Yan, Jinyue, 2021. "Kinetic energy harvesting technologies for applications in land transportation: A comprehensive review," Applied Energy, Elsevier, vol. 286(C).
    2. Gao, Mingyuan & Wang, Yuan & Wang, Yifeng & Yao, Ye & Wang, Ping & Sun, Yuhua & Xiao, Jieling, 2020. "Modeling and experimental verification of a fractional damping quad-stable energy harvesting system for use in wireless sensor networks," Energy, Elsevier, vol. 190(C).
    3. Gao, Mingyuan & Cong, Jianli & Xiao, Jieling & He, Qing & Li, Shoutai & Wang, Yuan & Yao, Ye & Chen, Rong & Wang, Ping, 2020. "Dynamic modeling and experimental investigation of self-powered sensor nodes for freight rail transport," Applied Energy, Elsevier, vol. 257(C).
    4. Azam, Ali & Ahmed, Ammar & Kamran, Muhammad Sajid & Hai, Li & Zhang, Zutao & Ali, Asif, 2021. "Knowledge structuring for enhancing mechanical energy harvesting (MEH): An in-depth review from 2000 to 2020 using CiteSpace," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    5. Zeadally, Sherali & Shaikh, Faisal Karim & Talpur, Anum & Sheng, Quan Z., 2020. "Design architectures for energy harvesting in the Internet of Things," Renewable and Sustainable Energy Reviews, Elsevier, vol. 128(C).

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