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Application Research on KPCA and PSO-SVM in Fault Diagnosis of Cage Asynchronous Traction Motor on Electric Locomotive
Pages: 44-49
Year: Issue:  2
Journal: Electrical Engineering Materials

Abstract: 提出了一种采用核主成分分析和粒子群优化支持向量机的电力机车笼型异步牵引电机故障诊断方法.先利用核主成分分析对故障数据进行特征提取,以获得的故障特征子集作为支持向量机故障分类器的训练样本,然后设计和构建了支持向量机多故障诊断系统.其中,支持向量机的参数通过粒子群优化算法进行了优化,最后实现对笼型异步牵引电机的故障诊断.实验结果分析表明,该方法能够有效地应用于电力机车笼型异步牵引电机的故障诊断.
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