基于TWSVM算法的发动机故障识别方法

Engine Fault Identification Based on TWSVM Algorithm

为了快速有效地诊断出汽油发动机故障,提出了一种基于孪生支持向量机(TWSVM)的发动机故障诊断方法.该方法利用HC、CO、CO2、O2和NOx共5种尾气参数值,并对其进行规范化处理,然后把这些数据作为特征向量,用于孪生支持向量机构成的多分类器中进行训练和测试,从而达到识别故障类别的目的.试验结果表明:采用孪生支持向量机分类方法比利用传统支持向量机具有更好的分类效果,且训练速度更快;在小样本数据情况下,故障诊断正确率可达到98.4%,能有效描述汽车尾气成分变化与发动机故障状态之间的复杂关系.

In order to diagnose the failure of gasoline engine quickly and effectively,an engine fault diagnosis method based on twin support vector machine was proposed.The method is to use the five exhaust gas parameters such as HC,CO,CO2,O2,NOx,and normalize the data,extract the eigenvector as the learning sample,and then use the multi-classifier based on the twin support vector machine to train and test,so as to achieve the purpose of identifying the type of failure.The experimental results show that the classification method of twin support vector machine has stronger classification ability than that of the traditional support vector machine,and the training speed is faster.In the case of less samples data,the correctness of fault diagnosis can reach 98.4%.This can effectively describe the complicated relationship between the car exhaust composition changes and the engine fault state.