APPLICATION OF GWO-SVM IN WIND TURBINE GEARBOX FAULT DIAGNOSIS

HU Xuan; LI Chun; YE KeHua

doi:10.16579/j.issn.1001.9669.2021.05.002

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APPLICATION OF GWO-SVM IN WIND TURBINE GEARBOX FAULT DIAGNOSIS

更新时间：2022-09-22

- APPLICATION OF GWO-SVM IN WIND TURBINE GEARBOX FAULT DIAGNOSIS
- Journal of Mechanical Strength Vol. 43, Issue 5, Pages: 1026-1034(2021)
- 作者机构：
  
  1. 上海理工大学能源与动力工程学院
  2. 上海市动力工程多相流动与传热重点实验室
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2021.05.002
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HU Xuan, LI Chun, YE KeHua. APPLICATION OF GWO-SVM IN WIND TURBINE GEARBOX FAULT DIAGNOSIS. [J]. 43(5):1026-1034(2021)
DOI：

HU Xuan, LI Chun, YE KeHua. APPLICATION OF GWO-SVM IN WIND TURBINE GEARBOX FAULT DIAGNOSIS. [J]. 43(5):1026-1034(2021) DOI： 10.16579/j.issn.1001.9669.2021.05.002.

摘要

针对风力机齿轮箱振动信号非线性和非平稳性的特征,提出基于模糊熵(Fuzzy Entropy,FE)和灰狼算法优化(Grey Wolf Optimizer,GWO)的支持向量机(GWO Support Vector Machine,GWO-SVM)的故障诊断方法。通过集合经验模态分解算法(Ensemble Empirical Mode Decomposition,EEMD)对振动信号进行分解得到若干本征模态函数(Intrinsic Mode Function,IMF)分量;求取各状态IMF分量的模糊熵并构建特征向量;将各特征向量输入GWO-SVM模型进行故障识别及分类。结果表明:齿轮箱振动信号不同状态下的模糊熵有一定区分度,通过GWO-SVM能对其进行精确识别和分类,且GWO-SVM相对于粒子群优化(Particle Swarm Optimization,PSO) SVM模型和遗传算法(Genetic Algorithm,GA)优化SVM模型具有更短的运行时间和更高准确率,平均准确率高达92.5%。

Abstract

Aiming at the nonlinear and instability characteristics of the wind turbine gearbox bearing fault signal,a method based on the Fuzzy Entropy and Grey Wolf Optimizer Support Vector Machine（ GWO-SVM） for the fault diagnosis of gearbox was proposed in this paper. Firstly,EEMD was used to decompose the vibration signal into the several intrinsic mode functions（ IMFs）. Secondly,calculated the IMFs’ fuzzy entropies in each state and constructed feature vectors. Finally,the vectors were adopted as the input parameters for the GWO-SVM to diagnose the fault. The results prove that the fuzzy entropy of gearbox vibration signals in different states has a certain degree of discrimination,it can be identified and classified by GWO-SVM accurately. Meanwhile,GWO-SVM is compared with PSO-SVM and GA-SVM,it has shorter time and higher accuracy,the mean accuracy can up to 92. 5%.

关键词

风力机齿轮箱故障诊断集合经验模态分解灰狼算法优化支持向量机模糊熵

Keywords

Wind turbine gearboxFault diagnosisEnsemble empirical mode decompositionGrey wolf optimizerSupport vector machineFuzzy entropy

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