基于EEMD-RVM风力发电机故障诊断方法研究

牛盛瑜; 张新燕; 杨璐璐; 邸强; 张冠琪

牛盛瑜,张新燕,杨璐璐,邸强,张冠琪.基于EEMD-RVM风力发电机故障诊断方法研究[J].电测与仪表,2018,55(19):01-06.
Niu Shengyu,Zhang Xinyan,Yang Lulu,Di Qiang,Zhang Guanqi.Research on fault diagnosis method of wind turbine based on EEMD-RVM[J].Electrical Measurement & Instrumentation,2018,55(19):01-06.

基于EEMD-RVM风力发电机故障诊断方法研究

Research on fault diagnosis method of wind turbine based on EEMD-RVM

DOI：

中文关键词: 综合经验模态分解风力发电机早期故障诊断相关向量机振动信号

英文关键词:ensemble empirical mode decomposition, wind turbine generator, early fault diagnosis, relevance vector machine, vibration signal

基金项目:国家自然科学基金项目（重点项目）

作者中文名	作者英文名	单位
牛盛瑜	Niu Shengyu	新疆大学电气工程学院
张新燕	Zhang Xinyan	新疆大学电气工程学院
杨璐璐	Yang Lulu	新疆大学电气工程学院
邸强	Di Qiang	新疆大学电气工程学院
张冠琪	Zhang Guanqi	新疆大学电气工程学院

摘要点击次数: 1783

中文摘要:

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,本文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。在实例中将本文所提方法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。

英文摘要:

Early fault characteristics of wind turbine generator is not obvious, less fault data can be collected, and the diagnosis accuracy is relatively low. A method of EEMD combined with RVM was proposed for early diagnosis by multiple faults of wind turbines. Firstly, pre-process the vibration signals of every faults of the wind turbine by EEMD which combined with the grey relational degree, and extract the optimal fault characteristics; then, train the fault characteristics by the RVM for establish the fault diagnosis model. Applying this method to a real diagnosis of wind turbine vibrating fault, by comparing the diagnostic results which obtained by WPD with RVM and EEMD with LS-SVM, the results show that the EEMD-RVM method is feasible and has the advantages of short time consuming and high precision.

查看全文查看/发表评论下载PDF阅读器

关闭