基于EEMD-RVM风力发电机故障诊断方法研究

TM93; 针对风力发电机早期故障表征不明显、能采集有效数据量较少、诊断结果精度较低等问题,文中提出一种运用综合经验模态分解(Ensemble Empirical Mode Decomposition,EEMD)结合相关向量机的方法对风力发电机多类故障进行早期诊断.首先,利用EEMD结合灰色关联度的方法对风机各类故障的振动信号进行预处理,提取最优故障特征;再通过相关向量机(Relevance Vector Machine,RVM)对提取的故障特征训练,并建立相应的故障诊断模型进行诊断.在实例中将文中所提方法EEMD-RVM与小波包分解(Wavelet Packet Decomposition,...

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Published in电测与仪表 Vol. 55; no. 19; pp. 1 - 19
Main Authors 牛盛瑜, 张新燕, 杨璐璐, 邸强, 张冠琪
Format Journal Article
LanguageChinese
Published 新疆大学电气工程学院,乌鲁木齐,830047 2018
Subjects
相关向量机
综合经验模态分解
风力发电机
早期故障诊断
振动信号
Online AccessGet full text
ISSN1001-1390

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Abstract TM93; 针对风力发电机早期故障表征不明显、能采集有效数据量较少、诊断结果精度较低等问题,文中提出一种运用综合经验模态分解(Ensemble Empirical Mode Decomposition,EEMD)结合相关向量机的方法对风力发电机多类故障进行早期诊断.首先,利用EEMD结合灰色关联度的方法对风机各类故障的振动信号进行预处理,提取最优故障特征;再通过相关向量机(Relevance Vector Machine,RVM)对提取的故障特征训练,并建立相应的故障诊断模型进行诊断.在实例中将文中所提方法EEMD-RVM与小波包分解(Wavelet Packet Decomposition,WPD)结合RVM以及EEMD结合最小二乘支持向量机LS-SVM(Least Square Support Vector Machine,LS-SVM)方法的诊断结果作对比,结果表明,EEMD-RVM方法具有可行性,且具有耗时短、精度高等优点.
AbstractList TM93; 针对风力发电机早期故障表征不明显、能采集有效数据量较少、诊断结果精度较低等问题,文中提出一种运用综合经验模态分解(Ensemble Empirical Mode Decomposition,EEMD)结合相关向量机的方法对风力发电机多类故障进行早期诊断.首先,利用EEMD结合灰色关联度的方法对风机各类故障的振动信号进行预处理,提取最优故障特征;再通过相关向量机(Relevance Vector Machine,RVM)对提取的故障特征训练,并建立相应的故障诊断模型进行诊断.在实例中将文中所提方法EEMD-RVM与小波包分解(Wavelet Packet Decomposition,WPD)结合RVM以及EEMD结合最小二乘支持向量机LS-SVM(Least Square Support Vector Machine,LS-SVM)方法的诊断结果作对比,结果表明,EEMD-RVM方法具有可行性,且具有耗时短、精度高等优点.
Author 牛盛瑜
邸强
张新燕
张冠琪
杨璐璐
AuthorAffiliation 新疆大学电气工程学院,乌鲁木齐,830047
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Author_FL Zhang Xinyan
Niu Shengyu
Yang Lulu
Di Qiang
Zhang Guanqi
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DocumentTitle_FL Research on fault diagnosis method of wind turbine based on EEMD-RVM
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Keywords 相关向量机
综合经验模态分解
风力发电机
早期故障诊断
振动信号
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Snippet TM93; 针对风力发电机早期故障表征不明显、能采集有效数据量较少、诊断结果精度较低等问题,文中提出一种运用综合经验模态分解(Ensemble Empirical Mode Decomposition,EEMD)...
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Title 基于EEMD-RVM风力发电机故障诊断方法研究
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