Customized wavelet denoising using intra- and inter-scale dependency for bearing fault detection

Abstract

Bearing fault detection is a challenging task, especially at the incipient stage. Wavelet denoising is widely recognized as an effective tool for signal processing and feature extraction. The wavelet denoising method by incorporating neighboring coefficients (NeighCoeff), which is proposed by Cai and Silverman, gives the better results than the traditional term-by-term approaches. However, this method only exploits intra-scale dependency of wavelet coefficients. It does not consider the inter-scale dependency of wavelet coefficients. In this paper, customized wavelet denoising using intra- and inter-scale dependency of wavelet coefficients is proposed for bearing fault detection. By designing the prediction operator and update operator, a customized wavelet based on the lifting scheme is constructed directly to match the transient properties of a given signal. The NeighCoeff denoising algorithm is improved by taking into account the intra- and inter-scale dependency of wavelet coefficients. The results of the application to bearing fault detection show that the proposed method is very effective.