Rolling bearing fault classifying method based on FOA-MKSVM (fruit fly optimization algorithm-multiple kernel support vector machine)

Abstract

The invention relates to a rolling bearing fault classifying method based on FOA-MKSVM (fruit fly optimization algorithm-multiple kernel support vector machine), and belongs to the technical field of fault diagnosis of a rolling bearing. The invention aims at providing a rolling bearing fault classifying method which is fewer in initialization parameters, simple for setting parameters, high in global search capability and high in classifying accuracy. The method is characterized by comprising the following steps: extracting characteristics of each vibration signal of the rolling bearing at various states; establishing a multiple-kernel kernel function to achieve the multinucleation of a support vector machine; adopting a training characteristic set as the input of the multiple kernel support vector machine (MKSVM), and carrying out the parameter optimizing for a penalty coefficient C, each kernel function parameter and a kernel function weight gamma m of the MKSVM by utilizing a fruit fly optimization algorithm (FOA); inputting a test characteristic set into an MKSVM model to be tested, and then obtaining the classifying accuracy of the rolling bearing at a normal state, an inner ring fault state, an outer ring fault state and a rolling body fault state. The rolling bearing fault classifying method has the advantages of fewer initialization parameters, simplicity in parameter setting, high global search capability and high classifying accuracy.

Description

technical field [0001] The invention relates to a rolling bearing fault classification method, which belongs to the technical field of rolling bearing fault diagnosis. Background technique [0002] Rolling bearings are one of the key components widely used in rotating machinery, and their working conditions directly affect the operation of the entire equipment [1] . Faults will affect production quality and efficiency, and serious production interruptions will result in huge economic losses. Therefore, research on rolling bearing fault diagnosis is particularly important [2] . [0003] In recent years, rolling bearing fault diagnosis and reliability evaluation methods have been emerging, among which intelligent diagnosis is one of the research hotspots. [2-3] . Expert systems, neural networks, fuzzy logic, rough sets, genetic algorithms, support vector machines, granular computing and other methods are important foundations for the realization of artificial intelligence,...

AI Technical Summary

Problems solved by technology

[0006] Since the actual state data of rolling bearings are generally unbalanced, there are certain limitations when using a single kernel function for classification, and the selection of multiple parameters for support vector machines is also blind, and the existing technology does not address the characteristics of the actual rolling bearing data and Constructing Multi-core Kernel Functions for Support Vector Machine Parameter Selection

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