Early Fault Detection Model for Rolling Bearing Based on an Iterative Tunable Q-Factor Wavelet Transform

Abstract

To reduce the adverse effect of incorrect parameters for the traditional iterative tunable Q-factor wavelet transform, this paper proposes an iterative tunable Q-factor wavelet transform method for fault feature extraction. Firstly, before decomposing the bearing vibration signal by an iterative tunable Q-factor wavelet transform, the initial values of 3 basic factors should be set: the quality factor Q, redundancy r and the number of decomposition level J. Secondly, the kurtosis of a high resonance component, which is the result of an iterative tunable Q-factor wavelet transform, is calculated through multistep iteration until it meets the iteration stop condition. Finally, the envelope spectrum of the final low resonance component is calculated, and the type of bearing fault can be recognized according to the frequency of extreme points. The results show that this method can effectively suppress noise and in-band interference and avoid fault identification inaccuracies caused by improper parameters and can also identify the fault feature frequency more clearly. 1