基于支持矢量聚类的机械故障诊断  

Fault Diagnosis of Machine Based on Support Vector Clustering

作　　者：王自营[1] 邱绵浩[1] 安钢[1] 

机构地区：[1]装甲兵工程学院机械工程系,北京100072

出　　处：《机械工程学报》2009年第4期226-230,共5页Journal of Mechanical Engineering

基　　金：国防科技重点实验室基金资助项目(51457050103JB3502)

摘　　要：针对无监督的支持矢量聚类方法由于样本类别数量未知带来的模型参数难以选择的问题,提出有监督的支持矢量聚类方法,并应用到机械故障诊断中。该方法首先以聚类区域个数及支持矢量个数作为模型参数的选择准则,以支持矢量为核估计样本分布的概率密度,并根据概率密度估计值选择不同聚类区域的类别代表样本,而后引入k近邻法实现对不同故障的分类。对测试样本的分类结果表明了该方法的有效性。Because the number of category of sample is unknown, the selection of parameters of model of unsupervised SVC becomes very difficult. Therefore, the supervised SVC method is put forward and applied to fault diagnosis of machine. The parameters of model are selected on the basis of the number of clustering regions and the number of SVs. The probability density of sample distribution is estimated on the basis of taking SV as the core, and typical samples of categories of different clustering regions are selected on the basis of estimated values of probability density. Different faults are classified by introducing k nearest neighbor. The result of classification shows that the SVC method is effective.

关 键 词：支持矢量聚类 故障诊断 k 近邻法 

分 类 号：TH17[机械工程—机械制造及自动化] TP18[自动化与计算机技术—控制理论与控制工程]