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Fault feature extraction method of bearing outer ring based on WOA-VMD combined with MOMEDA

Published:2023-11-27 author:WANG Yingying, CHEN Zhigang, WANG Yanxue. Browse: 275 Check PDF documents

Fault feature extraction method of bearing outer ring based on

WOA-VMD combined with MOMEDA

WANG Yingying1, CHEN Zhigang1,2, WANG Yanxue1

(1.School of Mechanical Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044,

China; 2.Beijing Construction Safety Monitoring Engineering Technology Research Center, Beijing 100044, China)

Abstract: In order to solve the problem that the rolling bearing working environment was more complex and the fault characteristic signal was easily affected by noise and difficult to be discriminated, a fault feature extraction method of bearing outer ring based on whale optimization algorithm (WOA) of variational modal decomposition (VMD) combined with multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) was proposed. First of all, the simulated signal was decomposed using VMD, and the optimal number of decomposition layers and the sample entropy of each component was determined using WOA. Then the optimal component containing the fault signal was obtained with the minimum value of sample entropy as the target search. The best component obtained was reconstructed by MOMEDA, and the fault characteristic frequency of the simulated signal and its multiplier frequency were obtained from the envelope spectrum of the reconstructed signal. Finally, In order to verify the effectiveness of WOA-VMD combined with MOMEDA, data were collected on the experimental platform, and the characteristics of outer ring fault signals of rolling bearings were extracted. The experimental results show that the method can be used to efficiently decompose the signal seeking, and can more accurately obtain the fault frequency of 100 Hz of the simulated signal and the approximate fault frequency of 87.5 Hz of the extracted signal of the experimental bench using the method, which verifies the effectiveness of the method. The research results show that WOA-VMD combined with MOMEDA can effectively extract the fault characteristic signal of rolling bearing under the condition of low signal-to-noise ratio and extract the fault characteristic frequency from the reconstructed signal.

Key words: fault signal decomposition; fault signal reconstruction; whale optimization algorithm (WOA); variational modal decomposition (VMD); sample entropy; multipoint optimal minimum entropy deconvolution adjusted (MOMEDA); fault characteristic frequency

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