Fault feature extraction method of rolling bearing based on parameter optimized VMD

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Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (1) : 86-94.

ZHENG Yi, YUE Jianhai, JIAO Jing, GUO Xinyuan

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Abstract

In early fault stage of rolling bearing, impulse components to reflect bearing fault features are easy to be submerged by strong background noise. Here, a rolling bearing fault feature extraction method based on correlated kurtosis (CK) and variational mode decomposition (VMD) was proposed to solve this problem. Aiming at the uncertainty of parameters in VMD, the grasshopper optimization algorithm (GOA) with CK as fitness function was proposed to adaptively select parameters of VMD. Aiming at the problem of selecting mode components of the fault signal after processed with the optimized VMD, CK was taken as the index, the mode component with the maximum CK index was selected to do envelope demodulation analysis, and extract fault feature information in bearing signal. The processing results of simulated and really measured signals showed that the proposed method can accurately extract weak features of rolling bearing fault signal under strong background noise.

Key words

rolling bearing / strong noise / variational mode decomposition (VMD) / correlated kurtosis (CK) / grasshopper optimization algorithm (GOA)

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ZHENG Yi, YUE Jianhai, JIAO Jing, GUO Xinyuan. Fault feature extraction method of rolling bearing based on parameter optimized VMD[J]. Journal of Vibration and Shock, 2021, 40(1): 86-94

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