Enhancement of fault vibration signature analysis for rotary machines using an improved wavelet-based periodic group-sparse signal estimation technique

In this paper, a wavelet-based periodic group-sparse signal denoising approach is proposed for detecting faults in rotary machines. The proposed approach exploits group sparsity in the wavelet domain. For this purpose, a periodicity-induced overlapping group shrinkage technique is utilized to threshold the wavelet coefficients. The wavelet coefficients are obtained by using the tunable Q-factor wavelet transform to decompose the measured vibration signals. The proposed approach is constrained to promote sparsity more strongly than convex regularization for estimating periodic group-sparse signals in noise, while avoiding nonconvex optimization. In addition, this maximally sparse convex approach has the advantage of preserving the oscillatory behavior of the useful fault features. A simulated signal is formulated to verify the performance of the proposed approach in periodic feature extraction. The detection performance of the proposed approach is compared with that of the comparative methods via root mean square error values. Finally, the proposed approach is applied to fault diagnosis of both experimental cases and engineering application. The processed results demonstrate that the proposed feature extraction technique can effectively detect the fault features from heavy background noise.