Construction of redundant second generation wavelet and mechanical signal feature extraction

To extract fault feature of mechanical signal corrupted by noise, a new method to construct redundant second generation wavelet (RSGW) is presented to extract fault feature in time domain. The initial prediction operator and initial update operator are interpolated with zero, and then the prediction operator and update operator at different decomposition scale are gained. Splitting operation is unnecessary for RSGW, and the approximation signal is predicted and updated directly, the length of approximation signal and detail signal for all scales remains identical, and the thresholds at different scales are selected according to the noise characteristics. Experimental and engineering results confirm the advantages of RSGW over the other wavelet methods for signal de-noising, and the fault features of bearing inner raceway damage and vibration excited by steam in high-pressure turbine of turbo-generator in time domain are desirably extracted by RSGW.