Weak multiple fault detection based on weighted morletwavelet-overlapping group sparse for rolling bearing fault diagnosis

Abstract

As one of the important parts of a mechanical transmission system, a rolling bearing often has multiple faults coexisting, and the mutual coupling between multiple faults poses a challenge for accurate diagnosis of rolling bearings. Aiming at the above problems, this paper proposes a weighted Morlet wavelet-overlapping group sparse (WOGS) algorithm for the multiple fault diagnosis of rolling bearings. On the basis of the overlapping feature of Morlet wavelet transform coefficients, a WOGS optimization model was initially constructed. Thereafter, the weight coefficients in the model were constructed by analyzing the impulse features of the signal. Thus, majorization-minimization was used to solve the optimization problem. A case study on weak multiple fault diagnosis of rolling bearings was performed to validate the effectiveness of the WOGS algorithm. Quantitative indexes are used to further discuss the extraction accuracies of different algorithms, and the results show that the proposed algorithm exhibits better performance than other algorithms.