Feature Extraction of Weak-Bearing Faults Based on Laplace Wavelet and Orthogonal Matching Pursuit

Abstract

The transient impact component of early bearing faults is not obvious, and the traditional basis function expansion method is poor in feature extraction under strong noise conditions. In this paper, a transient feature extraction technique is proposed based on Laplace wavelet and orthogonal matching pursuit algorithm and combined with sparse representation theory. First, the overcomplete and redundant Laplace wavelet dictionary is adopted to represent vibration signals in a sparse way. Then the Hilbert resonance demodulation method is employed to obtain the envelope spectrum of sparse representation signal. Finally, the coefficient calculation problem of sparse representation is solved by orthogonal matching pursuit (OMP) algorithm. Simulation examples and experimental example are used to examine the performance of the proposed method. The results show that the weak-bearing faults feature extraction can be effectively realized since the transient shock component can be identified. Furthermore, the effectiveness of the proposed method is verified by the cyclic multishock simulated signals as well as the practical rolling-bearing vibration signals. Moreover, the comparison studies are also carried out to show that the proposed method outperforms the traditional basis function expansion methods in weak fault feature extraction of bearing.