The research on rolling element bearing fault diagnosis based on wavelet packets transform

There has been a lot of research on diagnosing rolling element bearing faults using wavelet analysis, but almost all methods are not ideal for picking up fault signal characteristics under strong noise. Therefore, this paper proposes the auto-correlation and the cross-correlation fault diagnosis methods based on wavelet packets transform (WPT) de-noising which combine correlation analysis with WPT for the first time. These two methods compute the auto-correlation or the cross-correlation of the measured vibration signals, then de-noise by thresholding and compute the auto-correlation of maximal energy coefficients of WPT and FFT of energy sequence. The simulation results indicate both of the methods enhance the capabilities of fault diagnosis of rolling bearing and pick up the fault characteristics effectively.