Fault feature extraction based on morlet wavelet transform and singular value decomposition

Aiming at the feature extraction of Morlet wavelet transform results, the wavelet coefficient matrix obtained by the continuous Morlet wavelet transform is decomposed by singular value decomposition (SVD). The relationship among the singular value, the feature signal and the noise in the Morlet wavelet transform results is analyzed. Based on this relationship, the effective feature information of wavelet transform results can be clearly extracted by selecting appropriate singular values for SVD reconstruction. Further calculation is carried out to obtain the frequency-energy spectrum, and the shock feature can be extracted according to the peak position of this spectrum. Finally, the proposed method is applied to the fault feature extraction of bearing vibration signals and is compared with other methods. The results show that the proposed method can extarct the distinct fault waveforms and achieve a very good effect on fault feature extraction at a low signal-to-noise ratio (SNR). ©, 2014, South China University of Technology. All right reserved.