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

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