A new rotating machinery fault diagnosis method for different speeds based on improved multivariate multiscale fuzzy distribution entropy

To abstract the fault features from multivariate vibration signals of the rotating machinery under different speeds, an improved multivariate multiscale fuzzy distribution entropy (IMMFDE) is proposed. Based on multivariate empirical mode decomposition, the IMMFDE can determine the maximum scale adaptively, meanwhile eliminate the frequency aliasing and avoid the loss of potentially useful information in the multiscale process. The trait of IMMFDE is verified by calculating the sequences and their amplitude spectrums at each scale of the simulated multivariate signals. Further, the fault diagnosis method is proposed for the rotating machinery under different speeds based on IMMFDE. In the method, the statistical parameters and IMMFDE are calculated as fault feature set; then, support vector machine is used for fault diagnosis. Applying the method to two types of the rotating machinery multi-fault diagnosis under different speeds, the results show the proposed method can obtain better fault diagnosis results.