Bearing Defect Recognition Based on Optimal Impulse Response Wavelet Denoising Technique

Abstract

A rolling bearing defect detection and recognition technique based on optimal impulse response continuous wavelet transform (IRCWT) de-noising technique is proposed. In the proposed technique, the IRCWT is used as a tool for local time and frequency characteristics analysis, and the spectral kurtosis (SK) is used as the impulse index to extract transient pulse components from the processed rolling defect vibration data. Firstly, the IRCWT is used to process the sampling vibration data. And then SK is calculated to choose the best signal band-pass band from the time frequency analysis of vibration data, in order to restrain the interference noise and highlight the transient shock feature. Finally, the envelope spectrum is computed and the rolling bearing defect feature is extracted. The rolling element bearing defect recognition results show that the proposed approach can effectively detect the transient characteristics and distinguish the rolling element bearing localized defect.