Low-rank and periodic group sparse based signal denoising method for rolling bearing fault feature extraction

Abstract

Vibration signal analysis is a vital method of achieving rolling bearing fault feature extraction, but the fault impulses contained in the vibration signals are susceptible to interference from noise, making it difficult to extract fault features. In order to effectively extract the fault features of rolling bearings, an adaptive low-rank (LR) and periodic group sparse (AdaLRPGS) denoising method is proposed. Firstly, an AdaLRPGS model is constructed, which is not only an improvement of the classical sparse LR method, but also can effectively enhance the LR and periodic group sparsity of the failure impulses. Secondly, an adaptive period prior matching method is proposed to adaptively match the period of the fault impulses, and the moth flame optimization algorithm is utilized to adaptively search the regularization parameter of the AdaLRPGS model, and then the rule for determining the regularization parameter is summarized in the simulation analysis, which solves the dependence of the AdaLRPGS model on the period prior and regularization parameter. Finally, the solution procedure of the AdaLRPGS model is derived under the framework of the alternating direction method of multipliers. The simulated and measured signals are analyzed using the proposed method and compared with some advanced methods. The results show that the proposed method can extract the fault features of rolling bearings and has significant advantages compared with some advanced methods.