Hierarchical Frequency-Domain Sparsity-Based Algorithm for Fault Feature Extraction of Rolling Bearings

Abstract

Rolling bearings are the crucial parts in rotating machines and its fault detection is indispensable for ensuring operational reliability of entire mechanical system. To accurately extract fault-induced fault features from vibration signal, this article proposes a hierarchical frequency-domain sparsity-based algorithm (HFDSA). Concretely, the algorithm is established based on the frequency-domain sparsity property of bearing fault features, in which a customized penalty function incorporating weighting strategy is introduced for hierarchically penalizing sparse coefficients. In addition, an iterative convergence algorithm is deduced to solve optimization problem in the proposed method HFDSA and parameter selection method is discussed in detail. Finally, the effectiveness of the HFDSA is verified through the simulation analysis and case studies. The results demonstrate that the HFDSA can efficiently extract fault transients in noisy vibration signal and achieve fault detection of rolling bearings.