&nbsp;Fault feature extraction of rolling bearings based on VMD-SVD joint de-noising and FSWT

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Journal of Vibration and Shock ›› 2018, Vol. 37 ›› Issue (17) : 210-217.

Fault feature extraction of rolling bearings based on VMD-SVD joint de-noising and FSWT

MA Zengqiang,ZHANG Junjia,ZHANG An,RUAN Wanying

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Abstract

Aiming at problems of early fault features of rolling bearings being weak and the deficiency of the frequency sliced wavelet transformation (FSWT)’s extracting fault features under strong background noise,a new fault feature extracting method using FSWT and joint de-noising of the variational mode decomposition(VMD) and the singular value decomposition (SVD) was proposed. Firstly,VMD was used to decompose adaptively a fault signal into a series of intrinsic mode functions (IMFs). Some IMFs containing richest fault information were selected to reconstruct a signal based on the kurtosis criterion. Then SVD was used to de-noise the reconstructed signal again and improve its signal-noise ratio. Finally,FSWT was done for the reconstructed signal to highlight fault features’time-frequency distribution information and extract fault features. The analysis results of simulated signals and test data showed that this method can effectively eliminate noise effects and clearly extract the fault signal’s characteristic frequency to realize the accurate recognition of rolling bearings’faults.

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rolling bearing / VMD / SVD / FSWT / fault feature extraction

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MA Zengqiang,ZHANG Junjia,ZHANG An,RUAN Wanying. Fault feature extraction of rolling bearings based on VMD-SVD joint de-noising and FSWT[J]. Journal of Vibration and Shock, 2018, 37(17): 210-217

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