Compound fault diagnosis of rolling bearing based on AVME-OMOMEDA

Traditional algorithms are difficult to effectively separate and extract the composite fault features of bearings with overlapping resonance bands, an adaptive rolling bearing composite fault feature separation and extraction method combining adaptive variational mode extraction (AVME) and optimized multi-point optimal minimum entropy deconvolution adjusted (OMOMEDA) is proposed in this paper. The initial value of the center frequency of the VME parameter is determined by using the autocorrelation energy spectrum of S transform spectrum, and the desired modes related to the fault are extracted. Then the desired modes are linearly superimposed to reconstruct the original signal to realize the noise reduction of the signal. Extract periodic pulse signals from the reconstructed signal using OMOMEDA, and obtain fault characteristic frequencies by combining with envelope demodulation. The simulation and test signals verify that the method can effectively separate and extract the composite fault features of bearings with overlapping resonance bands. And compared with four other existing algorithms such as VMD-MCKD, the superiority of the proposed method is demonstrated.