Aiming at the problem that the signal features of rolling bearing fault diagnosis need to be extracted and selected manually, which affects the accuracy of fault classification, a rolling bearing fault diagnosis method based on LSTM Auto-Encoder is proposed in this paper. The Auto-Encoder can automatically learn useful features from the vibration signal. LSTM is used to process time series data. The LSTM network is used as the encoder and decoder of Auto-Encoder. Meanwhile, KL divergence is introduced to improve the loss function to better reconstruct the signal. Experiment shows that the proposed algorithm has good performance in multiclass classification, and puts forward a new direction for the automation and intelligence of rolling bearing fault diagnosis.


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    Titel :

    Bearing Fault Diagnosis Method of Bearing Based on LSTM Auto-Encoder


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Qin, Yong (Herausgeber:in) / Jia, Limin (Herausgeber:in) / Liang, Jianying (Herausgeber:in) / Liu, Zhigang (Herausgeber:in) / Diao, Lijun (Herausgeber:in) / An, Min (Herausgeber:in) / Lu, Zhencong (Autor:in) / Qin, Yong (Autor:in) / Cheng, Xiaoqing (Autor:in) / Zhang, Shunjie (Autor:in)

    Kongress:

    International Conference on Electrical and Information Technologies for Rail Transportation ; 2021 October 21, 2021 - October 23, 2021



    Erscheinungsdatum :

    23.02.2022


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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