Wavelet transform with spectral post-processing for enhanced feature extraction

The quality of machine condition monitoring techniques as well as their applicability in the industry are determined by the effectiveness and efficiency with which characteristic signal features are extracted and identified. Because of the weak amplitude and short duration of structural defect signals at the incipient stage, it is generally difficult to extract hidden features from the data measured using conventional spectral techniques. A new approach based on a combined wavelet and Fourier transformations is presented in this paper. Experimental studies on a rolling bearing with a localized point defect of 0.25 mm diameter has shown that this new technique provides significantly improved feature extraction capability over the spectral techniques.