Use of Composite Multivariate Multiscale Permutation Fuzzy Entropy to Diagnose the Faults of Rolling Bearing

The study focuses on the fault signals of rolling bearings, which are characterized by nonlinearity, periodic impact, and low signal-to-noise ratio. The advantages of entropy calculation in analyzing time series data were combined with the high calculation accuracy of Multiscale Fuzzy Entropy (MFE) and the strong noise resistance of Multiscale Permutation Entropy (MPE), a multivariate coarse-grained form was introduced, and the coarse-grained process was improved. The Composite Multivariate Multiscale Permutation Fuzzy Entropy (CMvMPFE) method was proposed to solve the problems of low accuracy, large entropy perturbation, and information loss in the calculation process of fault feature parameters. This method extracts the fault characteristics of rolling bearings more comprehensively and accurately. The CMvMPFE method was used to calculate the entropy value of the rolling bearing experimental fault data, and Support Vector Machine (SVM) was used for fault diagnosis analysis. By comparing with MPFE, the Composite Multiscale Permutation Fuzzy Entropy (CMPFE) and the Multivariate Multiscale Permutation Fuzzy Entropy (MvMPFE) methods, the results of the calculations show that the CMvMPFE method can extract rolling bearing fault characteristics more comprehensively and accurately, and it also has good robustness.

Automated summary

The study focuses on the fault signals of rolling bearings, characterized by nonlinearity, periodic impact, and low signal-to-noise ratio. A new method called Composite Multivariate Multiscale Permutation Fuzzy Entropy (CMvMPFE) was proposed to comprehensively and accurately extract fault characteristics from rolling bearings. The CMvMPFE method combines the advantages of entropy calculation, Multiscale Fuzzy Entropy (MFE), and Multiscale Permutation Entropy (MPE), addressing the problems of low accuracy, entropy perturbation, and information loss in fault feature parameter calculation.