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RSSD-CYCBD based on parameter adaptation and its application in feature extraction of bearing outer ring faults
Published:2024-05-24 author:LIU Hui, YAO Dechen, YANG Jianwei, et al. Browse: 567 Check PDF documents
RSSD-CYCBD based on parameter adaptation and its application in feature 
extraction of bearing outer ring faults


LIU Hui1,2, YAO Dechen1,2, YANG Jianwei1,2, WEI Minghui1,2


(1.School of Machineelectricity and Automobile Engineering, Beijing University of Civil Engineering and Architecture, Beijing 

100044, China; 2.Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles, Beijing University of Civil 
Engineering and Architecture, Beijing 100044, China)


Abstract:  In order to solve the problem that the working environment of rolling bearings is complex and the fault characteristic signals are easily masked by high-intensity noise. An improved fault diagnosis method for rolling bearings based on the combination of resonance sparse decomposition (RSSD) and maximum secondorder cyclic stationary blind deconvolution (CYCBD) was proposed. Firstly, the artificial gorilla troops optimization (GTO)algorithm was used to adaptively select the RSSD decomposition parameters by combining the fusion index of correlation coefficient and correlation kurtosis, and the optimal low resonance component of the simulation signal was obtained. Then, GTO combined with envelope entropy was used to adaptively select the cycle frequency and filter length of CYCBD, and the optimal low resonance component was deconvoluted, and the fault eigenfrequency of the signal was obtained from the envelope spectrum of the low resonance component. Finally, the effectiveness of the method was verified by using the data of Case Western Reserve University test bench and MFS-MG mechanical fault comprehensive simulation test bench, and the test results were compared with the RSSD-MCKD method. The results show that the proposed method can accurately obtain the fault frequency of the simulated signal of 20 Hz, the approximate fault frequency of the Case Western Reserve University test bench of 107.5 Hz, and the approximate fault frequency of the MFSMG test bench of 87.6 Hz, and the effect of fault feature extraction is better than that of RSSD-MCKD. The RSSD-CYCBD method can effectively identify the fault eigenfrequency and its frequency doubling, and realize fault diagnosis.

Key words: rolling bearings; fault diagnosis; resonance sparse decomposition(RSSD); maximum second-order cyclic smooth blind deconvolution (CYCBD); artificial force gorilla troops optimization algorithm (GTO); envelope entropy; high-intensity noise

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