Bearing condition is important for the operation safety of the trains. Traditional rule-based method can only detect the fault after the bearing is seriously damaged when the bearing temperature is far higher than the normal situation. In this paper, data driven bearing fault diagnosis of train is discussed. Taking the operation dynamics into account, a dynamic inner principal component analysis (DiPCA) based bearing fault monitoring method is proposed. After that, in order to locate the fault, a DiPCA based multi-directional reconstruction method is proposed to identify the possible faulty variables. Results from case studies using the data collected from a real train operation demonstrate the effectiveness of the proposed methods.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multi-direction Reconstruction for Fault Diagnosis of Train Bearings


    Contributors:
    Fang, Tong (author) / Liu, Qiang (author) / Cui, Dongliang (author)


    Publication date :

    2018-12-01


    Size :

    433928 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Fault Diagnosis of Train Wheelset Bearings Based on Improved Joint Distribution Adaptation

    Ding, Yifan / Xin, Ge / Li, Zhe et al. | British Library Conference Proceedings | 2022



    Fault Diagnosis of Train Wheelset Bearings Based on Improved Joint Distribution Adaptation

    Ding, Yifan / Xin, Ge / Li, Zhe et al. | Springer Verlag | 2022



    Fault diagnosis device, train control system and train

    CHEN JUNBO / LIU KEAN / GAN WEIWEI et al. | European Patent Office | 2023

    Free access