A Bearing Fault Diagnosis Method Based on Enhanced Singular Value Decomposition

Abstract

For the two shortcomings of singular value decomposition (SVD), the determination of the reconstruction order and the poor noise reduction ability, an enhanced SVD is introduced in this article. The core ideas include: first, an efficient method to determine the reconstructed order of SVD and the relative-change rate of the singular envelope kurtosis is presented, composed of improved SVD (ISVD). Then, the method to select the optimal node of wavelet packet transform (WPT) by the criterion of envelope kurtosis maximum is presented, composed of improved WPT (IWPT). The flexible filter design and superior noise reduction abilities of the IWPT and the passband denoise ability of the ISVD are organicly combined to form enhanced singular value decomposition (E-SVD) method. In addition, an indicator is introduced to evaluate the performance of the results. First, the reconstructed signal is obtained by performing ISVD on the original signal. Second, IWPT is executed on the reconstructed signal to achieve the optimal node. Finally, the filtered signal is combined with the envelope power spectrum to extract the bearing fault characteristic frequency. The method's validity and superiority are verified by the analysis of simulated data and actual cases of rolling bearing.