Singularity Detection Using AWT with Application to Fault Diagnosis

Singularity analysis of vibration signal is an effective method for mechanical fault diagnosis. Signal singularities can be characterized by its wavelet transforms modulus. Real wavelets are generally adopted in analysis. In fact, analytic wavelet transform (AWT) only reflects positive frequencies of signal and its modulus oscillation is weaker than real wavelet transform (RWT), so signal singularities can be detected more accurately. Singularities detection and de-noise based AWT are applied to vibration signals of running machines. Signals are analyzed by this method sampled under several conditions in a main reducer performance test bed developed by us. Experiment results show that singularity detection using the modulus maximum of an analytic wavelet is better than that of a real wavelet. The fault feature can be distinguished from the reconstructing signals more easily, which makes for fault features extraction.