Wavelet spectrum analysis for bearing fault diagnostics

A new signal processing technique, wavelet spectrum analysis, is proposed in this paper for incipient bearing fault diagnostics. This technique starts from investigating the resonance signatures over selected frequency bands to extract the representative features. A novel strategy is suggested for the deployment of the wavelet centre frequencies. A weighted Shannon function is proposed to synthesize the wavelet coefficient functions to enhance feature characteristics, whereas the applied weights are from a statistical index that quantifies the effect of different wavelet centre frequencies on feature extraction. An averaged autocorrelation spectrum is adopted to highlight the feature characteristics related to bearing health conditions. The performance of this proposed technique is examined by a series of experimental tests corresponding to different bearing conditions. Test results show that this new signal processing technique is an effective bearing fault detection method, which is especially useful for non-stationary feature extraction and analysis