Rolling element bearing faults diagnosis based on autocorrelation of optimized: wavelet de-noising technique

Abstract

Machinery failure diagnosis is an important component of the condition based maintenance (CBM) activities for most engineering systems. Rolling element bearings are the most common cause of rotating machinery failure. The existence of the amplitude modulation and noises in the faulty bearing vibration signal present challenges to effective fault detection method. The wavelet transform has been widely used in signal de-noising, due to its extraordinary time-frequency representation capability. In this paper, a new technique for rolling element bearing fault diagnosis based on the autocorrelation of wavelet de-noised vibration signal is applied. The wavelet base function has been derived from the bearing impulse response. To enhance the fault detection process the wavelet shape parameters (damping factor and center frequency) are optimized based on kurtosis maximization criteria. The results show the effectiveness of the proposed technique in revealing the bearing fault impulses and its periodicity for both simulated and real rolling bearing vibration signals.