Classification and Recognition Method for Bearing Fault based on IFOA-SVM

Wei Zhang; Zhihua Ma

doi:10.16578/j.issn.1004.2539.2021.02.023

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Classification and Recognition Method for Bearing Fault based on IFOA-SVM

Development·Application | 更新时间：2022-11-10

- Classification and Recognition Method for Bearing Fault based on IFOA-SVM
- Journal of Mechanical Transmission Vol. 45, Issue 2, Pages: 148-156(2021)
- 作者机构：
  
  1.西北工业大学机电学院，陕西西安 710072
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2021.02.023
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张维,马志华.基于IFOA-SVM的轴承故障分类识别方法[J].机械传动,2021,45(02):148-156.

Zhang Wei,Ma Zhihua.Classification and Recognition Method for Bearing Fault based on IFOA-SVM[J].Journal of Mechanical Transmission,2021,45(02):148-156.
张维,马志华.基于IFOA-SVM的轴承故障分类识别方法[J].机械传动,2021,45(02):148-156. DOI： 10.16578/j.issn.1004.2539.2021.02.023.

Zhang Wei,Ma Zhihua.Classification and Recognition Method for Bearing Fault based on IFOA-SVM[J].Journal of Mechanical Transmission,2021,45(02):148-156. DOI： 10.16578/j.issn.1004.2539.2021.02.023.

摘要

为了更好地准确识别轴承故障特征非线性分类问题，提出了一种基于IFOA-SVM的故障分类识别方法。使用变分模态分解方法对轴承振动信号进行分解处理，以模态分量的模糊近似熵和能量熵构成故障特征向量；基于“一对一”策略拓展设计了OVO-SVM多分类器，构造多项式核函数和径向基核函数组合的混合核函数，使用IFOA算法对SVM分类器的核函数比例系数, $λ$ ,http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=34101660&type=,http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=34101657&type=,2.28600001,2.62466669,、径向基核函数宽度参数, $σ$ ,http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=34101666&type=,http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=34101663&type=,2.45533323,2.62466669,、惩罚因子,C,等关键参数进行优化，构建IFOA-SVM故障分类识别模型；提出了轴承故障识别流程。结果表明，该方法可以实现对轴承故障特征准确高效的识别。

Abstract

In order to identify the nonlinear classification of bearing fault features more accurately， a fault identification method based on IFOA-SVM is proposed. Firstly， the variational mode decomposition method is used to decompose the vibration signals of the bearing， and the fuzzy approximate entropy and energy entropy of the modal component is used to form fault characteristics. Based on “one versus one” strategy， an OVO-SVM multi-classifier is designed， and the hybrid kernel function combined with polynomial kernel function and radial basis kernel function is constructed. Then， the key parameters such as the ratio coefficient of the kernel function , $λ$ ,http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=34101672&type=,http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=34101669&type=,2.28600001,2.62466669,， the width parameter of the radial basis kernel function , $σ$ ,http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=34101678&type=,http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=34101677&type=,2.45533323,2.62466669,， the penalty factor ,C, is optimized by IFOA algorithm， and the IFOA-SVM fault multi-classification identification model is built. The process of bearing fault identification is presented. Finally， the experimental results show that the method can realize accurate and efficient identification of the fault features of bearing.

关键词

变分模态分解改进果蝇优化算法支持向量机故障识别

Keywords

Variational mode decompositionImproved fruit-fly optimization algorithmSupport vector machineFault recognition

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