A sparsity-enhanced periodic OGS model for weak feature extraction of rolling bearing faults

Abstract

The fault symptom of rolling bearings is usually characterized by transient impulses formed at equal intervals, but the impulse signal is easily affected by noise and harmonic interferences, which increases the difficulty of extracting impulse features. In order to realize the effective extraction of weak periodic impulses under strong noise, this paper constructs a non-convex penalty function based on elastic net and Lp norm, and proposes a sparsity-enhanced periodic overlapping group shrinkage (POGS) method to detect rolling bearing faults. In the proposed sparse model, the internal function of the non-convex penalty function adopts the period-guided elastic net group sparse constraint, and the envelope autocorrelation function is used to dynamically update the period prior information to improve the extraction accuracy of highly correlated features within the group. Meanwhile, the non-convex Lp norm is introduced into the penalty function to constrain the sparsity of the overall variables, so as to guide the sparsity within and across groups (SWAG) of faults features while maintaining the weak impulse amplitudes. A comprehensive evaluation indicator is constructed as the fitness function of the moth-flame optimization (MFO) algorithm to realize automatic selection of model parameters. On the basis of the majorization-minimization (MM) algorithm and the improved soft threshold algorithm, the process of solving the objective function of the proposed model is given, and the performance of the proposed method is analyzed. The analysis results of the experimental data of rolling bearings suggest that in comparison with some existing sparse denoising methods, the proposed method exhibits better performance in the extraction of weak periodic impulses.