IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i9p4237-d1650928.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/9/4237/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/17/9/4237/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:9:p:4237-:d:1650928. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.