Sparse enhancement based on the total variational denoising for fault feature extraction of rolling element bearings

Abstract

As one of the essential parts of the mechanical transmission system, rolling bearing is vital to ensure the safe operation of mechanical equipment. The rolling bearing goes through four stages from its installation to the end of its life: normal operation, early weak failure, serious failure, and failure. If faults can be found in the early failure stage of the whole life cycle and maintenance strategies can be adopted in time, the safe and trouble-free operation of the equipment can be guaranteed. However, the fault features are not apparent in the early failure stage of the bearing’s full life cycle. Moreover, being completely submerged in strong background noise can easily occur, making early fault diagnosis challenging. This study presents a new sparse enhancement model based on kurtosis-wavelet total variation denoising (Kurt-WATV) for early fault feature extraction. Firstly, a sparse optimization model is constructed to extract the early fault feature, and the original signal is decomposed by the over-complete rational discrete wavelet transform (ORDWT). Then a fast iterative algorithm is deduced to solve the established sparse optimization model, and the optimal wavelet subband is selected by Kurt-WATV, which is reconstructed to the fault signal. Finally, the bearing test data from bearing’s full-life cycle are adopted to illustrate the effectiveness and robustness of the proposed method. Results confirm that the established method can achieve excellent performance in early fault feature extraction.