Weak fault feature extraction of rolling element bearing based on variational mode extraction and multi-objective information fusion band-pass filter

Abstract

Aiming at solving the difficulty in extracting early weak fault features of rolling element bearing (REB), a feature extraction method by combing variational mode extraction (VME) with multi-objective information fusion band-pass filter (MIFBF) is proposed. This method is based on the advantage of the VME in filtering out the interference signals and the enhancement effect of the MIFBF on the impact characteristic signals. Firstly, VME is used as the signal preprocessing method to filter out the interference noise. Then, the filtered signal is taken as the target signal, and then MIFBF is used to analyze it to obtain the optimal band-pass filter parameters-center Frequency and bandwidth, thereby constructing the optimal band-pass filter. Finally, further filter the target signal based on the constructed optimal band-pass filter and apply envelope demodulation spectral (EDS) analysis on the filtered signal. At last, satisfactory fault feature extraction effect can be achieved. To obtain key parameters of the optimal band-pass filter, this paper proposes a method based on time and frequency domain fusion indexes to balance the extraction of impact characteristics and cyclostationary characteristics while REB failure occurs. Compared with the existed single index based on time domain or frequency domain, better results could be obtained. Effectiveness and superiority of the proposed method are verified through simulation and experiments.

Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM

“…Xue et al [ 14 ] calculated the dispersion entropy of IMF components in different frequency bands and then used the joint approximate diagonalization of eigenmatrices (JADE) to extract fusion features and finally obtain the hierarchical discrete entropy (HDE) for bearing fault diagnosis. Wang et al [ 15 ] proposed a feature extraction method based on the combination of variational mode extraction (VME) and multi-objective information fusion band-pass filter (MIFBF). Yang et al [ 16 ] used the fractional Fourier transform (FRFT) algorithm to extract fault features from the original signals and then used stochastic resonance (SR) to enhance the weak fault feature information to complete bearing fault diagnosis according to the fault feature frequency.…”