基于主成分分析与支持向量机的汽轮机故障诊断

汽轮机故障诊断的一大难题是故障样本的缺乏,由于支持向量机针对小样本情况能取得很好的效果,为此,提出基于主成分分析与支持向量机的故障诊断方法,首先采用主成分分析方法对汽轮机故障数据进行故障特征提取,将特征向量作为支持向量分类器的输入,按照汽轮机的故障类型训练分类函数.对于支持向量机参数的选取,提出了基于错分样本数的蚁群优化算法.在小样本情况下对汽轮发电机组故障诊断进行了仿真研究.结果表明,应用该算法可以正确且有效地诊断多类汽轮机故障.

Fault Diagnosis of Turbine Generator Based on Pea and Svm

One of the problems that hamper the process of fault diagnosis of turbine generator is a lack of fault samples,moreover,SVM will achieve a very good results in the small sample situation,therefore,a new approach based on Support Vector Machine and Principal Component Analysis is proposed in this paper.First of all,PCA was used to extract the feature vectors of the fault samples,and then use these feature vectors as the input vectors of SVM classifier,moreover,the classification functions were trained accord...