Sparse representation of transients in wavelet basis and its application in gearbox fault feature extraction. (May 2015) - Fan, Wei; Cai, Gaigai; Zhu, Z.K.; Shen, Changqing; Huang, Weiguo; Shang, Li
Abstract: Vibration signals from a defective gearbox are often associated with important measurement information useful for gearbox fault diagnosis. The extraction of transient features from the vibration signals has always been a key issue for detecting the localized fault. In this paper, a new transient feature extraction technique is proposed for gearbox fault diagnosis based on sparse representation in wavelet basis. With the proposed method, both the impulse time and the period of transients can be effectively identified, and thus the transient features can be extracted. The effectiveness of the proposed method is verified by the simulated signals as well as the practical gearbox vibration signals. Comparison study shows that the proposed method outperforms empirical mode decomposition (EMD) in transient feature extraction. Highlights: A method of sparse representation in wavelet basis is proposed for gearbox fault feature extraction. The split augmented Lagrangian shrinkage algorithm is employed in sparse representation. Optimal wavelet atom is selected corresponding to the actual signal through correlation filtering. The effectiveness of the proposed method is verified by both of the simulated signals and the practical applications.