Rolling bearings are the key components of rail trains, and ensuring their normal operation is very important to the safety of trains. Aiming at the problem of bearing fault diagnosis, this paper proposes a bearing fault diagnosis algorithm based on adaptive CEEMD and cyclic spectrum coherence. Firstly, for noise reduction on the original signal, the adaptive CEEMD algorithm is proposed. This algorithm realizes the selection of the optimal parameter combination of CEEMD, solves the traditional EMD algorithm mode mixing problem, the reconstruction error problem of the EEMD algorithm, and the problem that CEEMD algorithm caused the difference of analysis results due to parameter settings. Secondly, the cyclic spectrum coherence analysis uses the characteristics of the cyclostationarity of the faulty bearing signal to reveal the hidden periodic characteristics in the signal, and calculates its improved envelope spectrum to make the fault characteristic information of the bearing more convenient to identify. Finally, this paper will compare the proposed algorithm with other current fault diagnosis algorithms to further verify the superiority of this algorithm.


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    Titel :

    Rolling Bearing Fault Diagnosis Method with Adaptive CEEMD and Cyclic Spectrum Coherence


    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) / Zeng, Yu (Autor:in) / Qin, Yong (Autor:in) / Yang, Wenlong (Autor:in) / Liu, Shengqiang (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 :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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