Due to the advantages of comfort and safety, high-speed trains are gradually becoming the mainstream public transport in China. Since the operating speed and mileage of high-speed trains have achieved rapid growth, it is more and more urgent to ensure their reliability and safety. As an important component in the bogies of high-speed trains, the health state of the bearing directly affects the operational safety of the trains. It is therefore necessary to diagnoze the faults of bearings in the bogies of high-speed trains as early as possible. In this paper, the bearing fault diagnostic methods for high-speed trains have been systematically summarized with their challenges and perspectives. First, it briefly introduces the structure of bearings in the bogies as well as the fault characteristic frequencies. Then, a brief review of the research on vibration-based signal processing methods and machine learning methods has been provided. Finally, the challenges and future developments of vibration-based bearing fault diagnostic methods for high-speed trains have been analyzed.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Vibration-based bearing fault diagnosis of high-speed trains: A literature review


    Beteiligte:
    Wanchun Hu (Autor:in) / Ge Xin (Autor:in) / Jiayi Wu (Autor:in) / Guoping An (Autor:in) / Yilei Li (Autor:in) / Ke Feng (Autor:in) / Jerome Antoni (Autor:in)


    Erscheinungsdatum :

    2023




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt





    Noise and vibration from high-speed trains

    Krylov, Viktor V. | TIBKAT | 2001


    A Newly Robust Fault Detection and Diagnosis Method for High-Speed Trains

    Chen, Hongtian / Jiang, Bin / Lu, Ningyun | IEEE | 2019


    A BRB-Based Effective Fault Diagnosis Model for High-Speed Trains Running Gear Systems

    Cheng, Chao / Wang, Jiuhe / Zhou, Zhijie et al. | IEEE | 2022


    Interior sound quality evaluation of high- speed trains -a literature review

    Qian, Kun / Shen, Zhenghua / Tan, Jing et al. | Taylor & Francis Verlag | 2025