Rolling bearing fault diagnosis method based on multivariate time-shifting multi-scale permutation entropy

Abstract

The invention discloses a rolling bearing fault diagnosis method based on multivariate time-shifting multi-scale permutation entropy, which belongs to the technical field of fault diagnosis. The method comprises the steps of firstly collecting an original fault vibration signal of a to-be-diagnosed object, extracting a multivariate time-shifting multi-scale permutation entropy value of the original fault vibration signal, conducting dimensionality reduction on the multivariate time-shifting multi-scale permutation entropy by adopting a Laplacian score method, dividing the dimension-reduced fault feature samples into a plurality of training samples and test samples, training a multi-fault feature classifier of the support vector machine based on bat algorithm optimization by adopting the plurality of training samples, classifying the test samples by adopting the trained multi-fault feature classifier, and finally identifying the fault type and the degree of the object according to the classification result. The method has relatively high innovativeness in processing multi-channel signals of signals acquired by the sensor, and has relatively high recognition degree in a fault recognition process.

Description

Technical field: [0001] The invention belongs to the technical field of fault diagnosis, in particular to a rolling bearing fault diagnosis method based on multivariate time-shift and multi-scale permutation entropy. Background technique: [0002] Equipment fault diagnosis technology has always been the focus of research in the field of fault diagnosis and maintenance. Since complex mechanical systems often exhibit nonlinear behavior during operation, vibration signals often exhibit nonlinear and non-stationary characteristics. The traditional linear analysis method is difficult to extract the obstacle feature information hidden in the vibration signal. Therefore, the nonlinear analysis method has become a hot spot that relevant scholars and technicians in the field of fault diagnosis technology continue to explore. In recent years, approximate entropy, sample entropy, fuzzy entropy, dispersion entropy, permutation entropy (Permutation entropy, PE), etc. have been widely us...

AI Technical Summary

Problems solved by technology

However, there is an immature coarse-graining process in the calculation process of multi-scale permutation entropy. In the process of coarse-graining, as the scale factor increases, the length of the time series obtained by coarse-graining will continue to decrease, resulting in the obtained permutation The deviation of the entropy value is increasing, which eventually leads to a decrease in the stability and accuracy of the diagnostic results

In addition, multi-scale permutation entropy can only deal with single-channel vibration signals, and is helpless for multi-channel vibration signals

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