Detection of signal transients based on wavelet and statistics for machine fault diagnosis
This paper presents a transient detection method that combines continuous wavelet transform (CWT) and KolmogorovSmirnov (KS) test for machine fault diagnosis. According to this method, the CWT represents the signal in the time-scale plane, and the proposed step-by-step detection based on KS test identifies the transient coefficients. Simulation study shows that the transient feature can be effectively identified in the time-scale plane with the KS test. Moreover, the transients can be further transformed back into the time domain through the inverse CWT. The proposed method is then utilized in the gearbox vibration transient detection for fault diagnosis, and the results show that the transient features both expressed in the time-scale plane and re-constructed in the time domain characterize the gearbox condition and fault severity development more clearly than the original time domain signal. The proposed method is also applied to the vibration signals of cone bearings with the localized fault in the inner race, outer race and the rolling elements, respectively. The detected transients indicate not only the existence of the bearing faults, but also the information about the fault severity to a certain degree.
transients fault diagnosis continuous wavelet transform ks test bearing gear