Optimal periodicity-enhanced group sparse for bearing incipient fault feature extraction

Abstract

Efficient and automatic fault feature extraction of rotating machinery, especially for incipient faults is a challenging task of great significance. In this article, an optimal periodicity-enhanced group sparse method is proposed. Firstly, a period sequence determination method without any prior information is proposed, and the amplitude is calculated by the numerical characteristics of the vibration signal to obtain period square waves. Secondly, the periodic square waves are embedded into the group sparse algorithm, to eliminate the influence of random impulses, and intensify the periodicity of the acquisition signal. Thirdly, a fault feature indicator reflecting both signal periodicity and sparsity within and across groups is proposed as the fitness of the marine predator algorithm for parameter automatic selection. In addition, the method proposed is evaluated and compared by simulation and experiment. The results show that it can effectively extract incipient fault features and outperforms traditional overlapping group shrinkage and Fast Kurtogram.