Aiming at the problem that the vibration signal of the bearing contains a lot of noise signals, a fault diagnosis method for rolling bearings based on the hybrid algorithm of EEMD and RCMDE is proposed. Firstly, the original signal is decomposed and reconstructed by EEMD, and then the reconstructed signal is analyzed by fine composite multi-scale scatter entropy to extract fault features. Finally, the kernel extreme learning machine is used for classification and identification. The example verification of the bearing data of Western Reserve University shows that the average recognition accuracy of fault diagnosis of the method in this paper is significantly higher than that of the other two methods, and the fault recognition accuracy for each state of the bearing is the highest.


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    Titel :

    Research on Rolling Bearing Diagnosis Based on EEMD and RCMDE Hybrid Algorithm


    Beteiligte:
    Yanyang, Li (Autor:in) / Jindong, Wang (Autor:in) / Haiyang, Zhao (Autor:in)


    Erscheinungsdatum :

    11.10.2023


    Format / Umfang :

    2650040 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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