Rolling bearing fault diagnosis method based on variation mode decomposition and permutation entropy

Abstract

The invention relates to a rolling bearing fault diagnosis method based on variation mode decomposition and permutation entropy. Vibration signals are decomposed with a variation mode decomposition method, so that reactive components and mode aliasing are effectively reduced, all the mode components include characteristic information of different time scales of original signals, and effective multi-scale components are provided for subsequent signal characteristic extraction. With the combination of the features that permutation entropy is simple in calculation, high in noise resisting ability and the like, bearing fault characteristics of all the mode components are extracted from multi-scale angles. Compared with single permutation entropy analysis of rolling bearing vibration, the characteristic information of the signals can be more comprehensively represented through the permutation entropy characteristic extracting method based on multiple scales, the recognition accuracy of a support vector machine is improved, and fault diagnosis of rolling bearings is better achieved.

Description

technical field [0001] The invention relates to a rolling bearing fault diagnosis method, in particular to a rolling bearing fault diagnosis method based on variational mode decomposition and permutation entropy. Background technique [0002] Rolling bearings are widely used components in mechanical equipment, and their operating status will directly affect the production efficiency and safety of equipment. In the actual operation of mechanical equipment, if the early failure of the rolling bearing cannot be found in time, the impact of the failure will accelerate the damage of the rolling bearing, and eventually lead to the failure of the rolling bearing, which will seriously affect the normal operation of the machine. Rolling bearings are one of the most fragile components in mechanical systems. About 30% of mechanical equipment failures are caused by local damage to rolling bearings (Chen Jin. Vibration monitoring and fault diagnosis of mechanical equipment [M]. Shanghai:...

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Problems solved by technology

[0006] The present invention is aimed at the problem of difficulty in extracting fault feature information in the fault diagnosis of rolling bearings, and proposes a rolling bearing fault diagnosis method based on variational mode decomposition and permutation entropy, combining the advantages of VMD on signal decomposition and permutation entropy to detect time series Random and dynamic mutation characteristics, better feature extraction and fault diagnosis of rolling bearings

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