Features of incipient faults are tiny in high-speed trains’ electrical drive systems. Noises and disturbances in the external environment and sensors can mask incipient faults. Therefore, fault detection (FD) of incipient faults is a challenge. This paper proposes a new FD scheme using a novel manifold learning method named local linear generalized autoencoder (LLGAE). The prominent characteristics of the LLGAE-based FD method are three-fold: 1) it can realize FD for electric drive systems even without the physical model or expertise; 2) it still has good results for non-Gaussian electrical drives; 3) it entirely takes into account the locally linear structure of samples. Mathematical derivations have proved the proposed method. Through an experimental platform of high-speed trains, the proposed method is validated.


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    Titel :

    Local Linear Generalized Autoencoder-Based Incipient Fault Detection for Electrical Drive Systems of High-Speed Trains


    Beteiligte:
    Cheng, Chao (Autor:in) / Ju, Yunfei (Autor:in) / Xu, Shuiqing (Autor:in) / Lv, Yisheng (Autor:in) / Chen, Hongtian (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.11.2023


    Format / Umfang :

    1193989 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






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