Bearing fault diagnosis based on Shannon Entropy and wavelet package decomposition

A new feature extraction method based on WPD and Entropy is proposed in this paper. Firstly, WPD is utilized to decompose the signal into different frequency bands to obtain different frequency sub-signal. Secondly, root-mean-squire (RMS) value, kurtosis (K) and peak factor (PF) parameters are extracted from each sub-signal to obtain the fault feature vector. Thirdly the Entropy of each feature vector is calculated to realize the bearing fault diagnosis. Finally, experimental results indicate that the bearing fault diagnosis method proposed in this paper is effective.