In the case of motor bearing failure, aiming at the weak bearing fault characteristics in stator current, this paper takes advantage of the sensitivity of cyclic autocorrelation function for initial fault characteristics and proposes a motor bearing fault detection method based on cyclic autocorrelation function analysis. Firstly, according to the torque fluctuation model, the stator current expression of bearing outer raceway fault is deduced. Then, the current expression is added to the cyclic autocorrelation function, and the result shows that the proposed method can not only demodulate the fault characteristic frequency, but also the amplitude of the fault characteristic signal will be maximized by selecting the appropriate slicing position, which is beneficial to the fault identification. Finally, the experimental platform is built, and the fault characteristics are extracted effectively by using the proposed method, and its performance is obviously better than that of the traditional power spectrum analysis.


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    Title :

    Research on Motor Bearing Fault Detection Method Based on Cyclic Autocorrelation Function Analysis


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Jia, Limin (editor) / Qin, Yong (editor) / Liu, Baoming (editor) / Liu, Zhigang (editor) / Diao, Lijun (editor) / An, Min (editor) / Wang, Peng (author) / Qiu, Chidong (author) / Wu, Xinbo (author) / Xue, Zhengyu (author)

    Conference:

    International Conference on Electrical and Information Technologies for Rail Transportation ; 2019 ; Qingdao, China October 25, 2019 - October 27, 2019



    Publication date :

    2020-04-08


    Size :

    8 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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