Sparse representation of gearbox compound fault features by combining Majorization-Minimization algorithm and wavelet bases

Abstract

Bearing and gear are essential components in gearbox, which is easily damaged and breaks down. Thus gearbox compound fault diagnosis has become a challenging topic in recent decades. Both bearing and gear faults in the gearbox tend to result in different transient impulse responses in the vibration signal and therefore it is necessary to present a method whose main task is to extract the different fault features. In this paper, a sparse representation method combining majorization minimization (MM) algorithm and different wavelet bases is proposed to resolve the problem. Through the proposed method, different transients buried in the noisy signal can be converted into sparse coefficients. Both the simulation study and the practical application show the proposed method is effective in extracting gearbox compound fault features.