An improved sparsity-enhanced decomposition signal method based on GMC and TQWT for rolling bearing faults

Abstract

Mechanical equipment is always exposed to poor working environments, such as high humidity, high temperature and heavy loads, which may lead to serious damage in key components. It is critical to identify the initial fault in time to avoid huge economic losses and casualties. In extracting the fault characteristics of a rolling bearing, its characteristic frequency is always disturbed by strong noise. In order to accurately separate the fault features from the strong noisy signal, an improved sparsity-enhanced decomposition signal method using the nonconvex penalty term of generalized minimax-concave and the dictionary term of tunable Q-factor wavelet transform is presented in this paper. An adaptive method for selecting regularization parameters is presented to subtly minimize the signal-to-noise ratio and root mean square error. Moreover, in order to reduce calculation cost, the forward–backward splitting algorithm is employed to maintain the convexity of the proposed sparsity. A simulation study and two practical fault experiments are used to validate the effectiveness of the proposed method in rolling bearing faults.