GMC sparse enhancement diagnostic method based on the tunable Q-factor wavelet transform for detecting faults in rotating machines

In mechanical fault diagnosis, weak fault feature extraction via traditional method is easily disturbed by strong background noises. In order to enhance the feature extraction performance, a novel sparsity-enhanced signal denoising method based on the combination of generalized minimax-concave penalty and tunable Q-factor wavelet transform is developed in this paper. Aiming at matching the transient fault features, the proposed method constructs an effective sparse optimization objective function for mechanical fault diagnosis. It is also proved that the non-convex controllable parameters can guarantee the overall convexity of the objective function under certain constraints. The proximal algorithm is used to solve the unconstrained optimization problem. Finally, the results of the simulation and practical fault experiment verify the effectiveness of the proposed method in the machinery fault diagnosis.