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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    Title :

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


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Qin, Yong (editor) / Jia, Limin (editor) / Liang, Jianying (editor) / Liu, Zhigang (editor) / Diao, Lijun (editor) / An, Min (editor) / Lu, Zhencong (author) / Qin, Yong (author) / Cheng, Xiaoqing (author) / Zhang, Shunjie (author)

    Conference:

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



    Publication date :

    2022-02-23


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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