结合C-均值聚类的自适应共振神经网络在风电机组齿轮箱故障诊断中的应用  

Application of ART2 Neural Network Combined with C-means Clustering in Fault Diagnosis of Wind Turbine Gearbox

作　　者：李状[1] 柳亦兵[1] 马志勇[1] 滕伟[1] 

机构地区：[1]华北电力大学能源动力与机械工程学院,北京102206

出　　处：《动力工程学报》2015年第8期646-651,665,共7页Journal of Chinese Society of Power Engineering

基　　金：国家自然科学基金资助项目(51305135);中央高校基本科研业务费专项资金资助项目(2014XS15);中国华能集团公司科技资助项目(HNKJ13-H20-05)

摘　　要：提出了一种结合C-均值聚类的自适应共振(Adaptive Resonance Theory 2,ART2)神经网络无监督学习分类方法,用于风电机组齿轮箱设备群的故障诊断.利用某风电场齿轮箱运行数据,采用ART2神经网络对样本数据进行初步分类,再采用C-均值聚类算法对神经网络分类结果进行修正,得到最终诊断结果,并与ART2神经网络分类结果进行了比较.结果表明:所提出的方法解决了原始神经网络算法存在"硬竞争"导致分类精度下降的问题,准确度高于传统的ART2神经网络,可以准确识别出故障齿轮箱.An unsupervised learning classification method by using adaptive resonance theory 2 （ART2） neural network combined with C-means clustering was proposed for fault diagnosis of wind turbine gearbox group. For operation data of gearboxes in a wind farm, ART2 neural network was used to preliminarily classify the samples, and subsequently C-means clustering algorithm was used to modify the classification results of ART2 neural network, and the final classification results were compared with those of ART2 neural network. Results show that the proposed method has higher accuracy than traditional ART2 neural network, since it solves the problem of low classification accuracy caused by hard competition of ART2, which therefore can identify the faulted gearbox accurately.

关 键 词：风电机组齿轮箱 自适应共振神经网络 C-均值聚类 无监督学习 故障诊断 

分 类 号：TH165.3[机械工程—机械制造及自动化]