Abstract
Rolling-element bearings are widely used in various mechanical and electrical systems. A reliable online bearing fault-diagnostic technique is critically needed to prevent the system's performance degradation and malfunction. In this paper, an extended wavelet spectrum analysis technique is proposed for a more positive assessment of bearing health conditions. Two strategies have been suggested for different wavelet function implementation. Two statistical indexes are proposed to quantify the resulting wavelet (coefficient) functions. Based on the information provided by these indexes, the wavelet functions can be deployed more effectively over the designated frequency bands. An extended Shannon function is proposed to synthesize the wavelet coefficients over selected bandwidths to enhance feature characteristics. An averaged autocorrelation power spectrum is adopted to highlight bearing characteristics. The viability of the developed technique is verified by online experimental tests corresponding to different bearing conditions.
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