Sparsity-enabled signal decomposition using tunable Q-factor wavelet transform for fault feature extraction of gearbox
A sparsity-enabled feature extraction method is proposed for gearbox fault diagnosis. The oscillatory behaviors of vibration signals are investigated by using TQWT. Vibration signals are sparsely decomposed by using MCA. The effects of two key parameters of the proposed method are analyzed. The effectiveness of the proposed method is verified by both of the simulated and practical applications.
sparsity-enabled signal decomposition tunable q-factor wavelet transform gearbox fault diagnosis morphological component analysis split augmented lagrangian shrinkage algorithm