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Data-Driven Online State Prediction Method for the Traction Motors of Electric Multiple Units (EMUs)

Author

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  • Yuchen Liu

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Chaoxu Li

    (China Academy of Railway Sciences Corporation Limited, Beijing 100081, China)

  • Man Li

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

Abstract

With the high-density operations of high-speed trains, predicting the health status of core components such as traction motors is crucial for enhancing the safety and sustainability of trains. Currently, traditional maintenance mechanisms such as periodic inspections and fixed-threshold alarm systems are hindered by delayed abnormality detection and inadequate real-time responsiveness. This paper proposes a dynamic prediction method for traction motor states based on an Online Gated Recurrent Unit (OGRU), which considers various influencing factors and updates model parameters in real-time. Experimental results demonstrate that the online prediction model significantly reduces the R M S E compared to offline methods and exhibits increased prediction stability under different conditions and step sizes. Notably, it decreases computational time by 23.3% relative to the Online Long Short-Term Memory (OLSTM) approach. The proposed method enhances preventive maintenance strategies, optimizes resource utilization, extends equipment lifespan, and reduces costs, thereby making a substantial contribution to the sustainable operation of high-speed railways. By improving energy efficiency, safety, and economic viability, this approach supports a transition toward greener rail transportation. Based on this study, the developed method can facilitate real-time maintenance decision-making, enabling the intelligent operation and maintenance of high-speed trains.

Suggested Citation

  • Yuchen Liu & Chaoxu Li & Man Li, 2025. "Data-Driven Online State Prediction Method for the Traction Motors of Electric Multiple Units (EMUs)," Sustainability, MDPI, vol. 17(9), pages 1-22, May.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:9:p:4237-:d:1650928
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    References listed on IDEAS

    as
    1. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    2. Wang, Fu-Kwun & Amogne, Zemenu Endalamaw & Chou, Jia-Hong & Tseng, Cheng, 2022. "Online remaining useful life prediction of lithium-ion batteries using bidirectional long short-term memory with attention mechanism," Energy, Elsevier, vol. 254(PB).
    3. Wang, Shunli & Takyi-Aninakwa, Paul & Jin, Siyu & Yu, Chunmei & Fernandez, Carlos & Stroe, Daniel-Ioan, 2022. "An improved feedforward-long short-term memory modeling method for the whole-life-cycle state of charge prediction of lithium-ion batteries considering current-voltage-temperature variation," Energy, Elsevier, vol. 254(PA).
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