Feature Extraction Based on Morlet Wavelet and its Application for Mechanical Fault Diagnosis

The vibration signals of a machine always carry the dynamic information of the machine. These signals are very useful for the feature extraction and fault diagnosis. However, in many cases, because these signals have very low signal-to-noise ratio (SNR), to extract feature components becomes difficult and the applicability of information drops down. Wavelet analysis in an effective tool for signal processing and feature extraction. In this paper, a denoising method based on wavelet analysis is applied to feature extraction for mechanical vibration signals. This method is an advanced version of the famous “soft-thresholding denoising method” proposed by Donoho and Johnstone. Based on the Morlet wavelet, the time-frequency resolution can be adapted to different signals of interest. In this paper, this denoising method is introduced in detail. The results of the application in rolling bearing diagnosis and gear-box diagnosis are satisfactory.