改进的SVM用于主变流器故障诊断

Improved SVM for Fault Diagnosis of Main Converter of Locomotive

针对机车主变流器故障,借助Matlab中的Simulink工具箱,仿真得到了主变流器发生不同故障时相应的电路输出电压;采用小波分析对输出电压进行信号处理,提取故障特征,构造特征向量,获得蕴涵电路故障信息的样本;利用SVM工具箱构建多故障分类器,并对故障样本进行诊断。由于SVM在无限空间建立分类超平面,容易导致误分类,针对此问题,提出了改进方法,使SVM在确定的有限空间进行分类。最后用仿真样本进行了训练和测试。实验结果表明诊断效果良好,实现了预期目标。

This paper was focused on the faults of main convener with the Simulink toolbox of Matlab, the output voltages of different faults had been obtained. Making use of wavelet analysis to process the output voltage, the fault feature had been extracted and the feature vector had been constructed. The samples had been accessed, which was containing the fault information with the SVM toolbox, the multi-fault classifiers had been established and the samples had been diagnosed. However, in the infinite space, SVM easily lead to wrong classification. For this problem, an improved method had been proposed, which cought make SVM to classify in a limited space. Finally, the simulation samples had been trained and tested. Experimental results show that the classification result is fine and achieves the desired objective.