A method for rolling bearing fault feature extraction based on parametric optimization VMD

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Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (21) : 195-202.

ZHENG Yuan, HU Jianzhong, JIA Minping, XU Feiyun, TONG Qingjun

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Abstract

Aiming at the problem of only using a single penalty factor in traditional variational mode decomposition (VMD) being difficult to extract fault features of rolling bearing in practical application, a method forrolling bearing fault feature extraction based on parametric optimization VMD was proposed.Firstly, the number of decomposition layers K was determined according to the maximum kurtosis criterion.Secondly, the penalty factor corresponding to each mode was optimized with the whale algorithm to realize adaptive selection of each mode’s optimal penalty factor, and obtain a vibration signal’s the optimal mode decomposition.Finally, the kurtosis criterion was used to screen the decomposed modal components, perform envelope demodulation, and extract bearing fault features.The improved VMD was used to analyze simulated signals and engineering actual data.Results showed that compared with the traditional VMD, EEMD and the fast speed spectral kurtosis method, the proposed method can effectively improve the sensitivity of fault feature extraction; it is valuable in engineering applicationto a certain extent.

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rolling bearing / variational mode decomposition (VMD) / whale algorithm / feature extraction

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ZHENG Yuan, HU Jianzhong, JIA Minping, XU Feiyun, TONG Qingjun. A method for rolling bearing fault feature extraction based on parametric optimization VMD[J]. Journal of Vibration and Shock, 2020, 39(21): 195-202

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