Fault Diagnosis of Fan Bearings Based on an Improved Grey Wolf Optimization Algorithm and SVM

Liu Jinyan; Abulizi Maimaitireyimu; Xiang Zhicheng; Xie Lirong

doi:10.16578/j.issn.1004.2539.2023.09.022

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Fault Diagnosis of Fan Bearings Based on an Improved Grey Wolf Optimization Algorithm and SVM

Development·Application | 更新时间：2023-09-18

- Fault Diagnosis of Fan Bearings Based on an Improved Grey Wolf Optimization Algorithm and SVM
- Journal of Mechanical Transmission Vol. 47, Issue 9, Pages: 160-169(2023)
- 作者机构：
  
  新疆大学电气工程学院，新疆乌鲁木齐 830046
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2023.09.022
  CLC：
- Published：15 September 2023，
  
  Received：16 June 2022，
  
  Revised：19 July 2022，
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刘谨言,买买提热依木·阿布力孜 ,项志成等.基于改进的灰狼优化算法与SVM风机轴承故障诊断[J].机械传动,2023,47(09):160-169.

Liu Jinyan,Abulizi Maimaitireyimu,Xiang Zhicheng,et al.Fault Diagnosis of Fan Bearings Based on an Improved Grey Wolf Optimization Algorithm and SVM[J].Journal of Mechanical Transmission,2023,47(09):160-169.
刘谨言,买买提热依木·阿布力孜 ,项志成等.基于改进的灰狼优化算法与SVM风机轴承故障诊断[J].机械传动,2023,47(09):160-169. DOI： 10.16578/j.issn.1004.2539.2023.09.022.

Liu Jinyan,Abulizi Maimaitireyimu,Xiang Zhicheng,et al.Fault Diagnosis of Fan Bearings Based on an Improved Grey Wolf Optimization Algorithm and SVM[J].Journal of Mechanical Transmission,2023,47(09):160-169. DOI： 10.16578/j.issn.1004.2539.2023.09.022.

摘要

针对当前风机轴承故障诊断准确率较低、诊断难度较大、耗时较长等问题，提出改进的灰狼优化（Improved Grey Wolf Optimization，IGWO）算法与支持向量机（Support Vector Machine，SVM）故障诊断方法。为了能够精准地提取故障特征，采用时频域分析中的小波包分解法对故障振动信号进行特征提取，将小波包分解后的8个频带能量作为故障特征并构建特征向量；建立SVM故障模型并利用IGWO算法对SVM模型进行参数寻优，避免了灰狼优化（Grey Wolf Optimization，GWO）算法后期易陷入局部最优、收敛速度过慢等。实验结果表明，IGWO算法平均故障识别率高达99.3%，能够更加快速、高效、准确地识别故障的类型，为故障诊断的发展提供了良好的技术支撑。

Abstract

To solve the problems of low accuracy

difficult diagnosis and long time consuming of the current fan bearing fault diagnosis

an improved grey wolf optimization (IGWO) algorithm and a support vector machine (SVM) fault diagnosis method are proposed. In order to accurately extract the fault features

the wavelet packet decomposition method in the time-frequency domain analysis is used to extract the fault vibration signal. Take the wavelet packet decomposition energy of the eight frequency bands as the fault feature， the eigenvectors are constructed. Then

the fault model of SVM is established and the parameters of the SVM model are optimized by the IGWO algorithm

which avoids the defects of local optimum and slow convergence. According to the experimental analysis result

the average fault recognition rate of the IGWO algorithm is up to 99.3%

and it can identify fault types more quickly

more efficiently and more accurately

which provides a good support for the development of fault diagnosis.

关键词

支持向量机改进的灰狼优化算法小波包分解特征提取故障分类

Keywords

Support vector machineImproved grey wolf optimization algorithmWavelet packet decompositionFeature extractionFault classification

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