A sparse measurement matrix based method for feature enhancement of bearing fault signal

Abstract

In the process of bearing signal acquisition, the overloaded data often causes great pressure on the equipment. In order to solve this problem, compressed sensing theory is used to collect signals, and the measurement matrix is sparsed into a sparse measurement matrix and a base measurement matrix. The fusion of the sparse dictionary and the measurement matrix expands the selections of the measurement matrix and improves the reconstruction accuracy. For the phenomenon of the local aggregation of matrix energy caused by the initialization of the sparse measurement matrix, the method of segmented reconstruction is proposed to solve it. Then, in view of the situation that the useful information in the reconstructed signal is polluted by a lot of noise, a new wavelet threshold denoising method is proposed, which is fused with the empirical wavelet transform (EWT), and the margin index and the peak-to-peak value are used as sensitive parameters to adjust the threshold, the effect of denoising and fault feature enhancement are performed simultaneously. Through two-step strategy, the effective acquisition of bearing signals and the enhancement of fault features are realized. The performance of this method is verified by simulation and actual bearing data and the results are encouraging.