Extracting symptoms of bearing faults in the wavelet domain

Abstract

Abstract

Improving the signal-to-noise (S/N) ratio is one of the classic machinery failure diagnostic problems. A new failure detection procedure using a wavelet-based estimator is proposed for extracting the symptoms of bearing faults under environmental noise conditions. The paper discusses the relationship between de-noising efficiency and S/N ratio under different threshold values and wavelet decompositions. Efficient ranges of wavelet coefficients and levels of wavelet decomposition for extracting the symptoms of bearing faults are obtained. A comparison of the failure detection capabilities of an envelope detector and a wavelet-based estimator under significant environmental noise is performed and the results show that the wavelet-based estimator is an effective method for extracting the symptoms of a bearing fault under such conditions.