Rolling bearing health condition evaluation method based on CFOA-MKHSVM

Abstract

The invention discloses a rolling bearing health condition evaluation method based on CFOA-MKHSVM, belongs to the technical field of bearing fault evaluation and aims at evaluating rolling bearing performance degradation degree more effectively. The method includes: extracting time domain and frequency domain statistical features of bearing vibration signals and wavelet-packet-based time frequency features; aiming at the problems of nonuniform state data distribution and data heterogeneity of a rolling bearing, adopting a hyper sphere support vector machine for recognition and performing multinuclear convex combination and optimization; in order to eliminate blindness of artificial selection of multiple parameters of a classifier and proneness to selecting into a local optimum problem, combining a fruit fly algorithm with a chaos theory to optimize the multiple parameters; building a chaos optimization fruit fly algorithm-multi-core hyper sphere support vector machine CFOA-MKHSVM model, and putting forward a normalized difference coefficient evaluation index. Experiments for comparing the normalized difference coefficient evaluation index with an SVDD algorithm evaluation index verify effectiveness of the normalized difference coefficient evaluation index, and quantitative evaluation of rolling bearing health state is realized.

Description

technical field [0001] The invention relates to a method for evaluating the health state of a rolling bearing, which belongs to the technical field of bearing fault evaluation. Background technique [0002] Rolling bearings are key rotating parts of mechanical equipment and one of the most vulnerable parts, and their operating status directly affects the working status of the entire equipment [1]. The performance degradation evaluation of rolling bearings is based on fault diagnosis technology, and the whole process from the intact state to a series of different degradation states is described and modeled, so as to realize the quantitative evaluation of the health status of rolling bearings [2-3]. [0003] The research on the performance degradation evaluation technology of rolling bearings has attracted the attention of many scholars. The Intelligent Maintenance System Research Center established in the United States, the University of Manchester, the University of Southamp...

AI Technical Summary

Problems solved by technology

[0007] The existing support vector data description (SupportVectorDataDescription, SVDD) based rolling bearing performance degradation evaluation technology cannot correctly evaluate the deep degradation state of the bearing, and when the fault occurs and the fault point is worn out after a relatively smooth service stage, the SVDD evaluation index is as follows The overlapping range of the evaluation value of the concave stage and the initial fault state is large, and the downward trend is too large, which is very easy to cause the problem of state identification error

There is no proposal in the prior art to evaluate the health status of rolling bearings by constructing the CFOA-MKHSVM model

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