摘要
汽车变速器早期故障监测与诊断对汽车安全行驶和降低交通事故起着非常重要的作用。针对汽车无级变速器故障特征,提出一种基于互信息熵(Mutual Information Entropy,MIE)理论的故障信号特征提取方法,以及易于实现的多类分类支持向量机(Support Vector Machine,SVM)算法处理故障状态分类问题。结果表明,将MIE和SVM算法相结合用于汽车无级变速器故障诊断方面是可行的和有效的,并能提高故障监测的可靠性。
The fault inspection and diagnosis of vehicle transmission plays a important role of safety traffic and knocking the traffic accidents down.For the fault character of vehicle continuously variable transmission（CVT）,the fault signal character extract entropy method based on the theory of mutual information entropy and the multiclass classification support vector machine（SVM） algorithm to be easily implemented to deal with the problem of classification for fault state are proposed.Experimental results show that the combination mutual information entropy with multiclass classification SVM algorithm in fault diagnosis of vehicle CVT is the feasibility and efficiency,and the reliability of fault inspection is improved.
出处
《机械传动》
CSCD
北大核心
2010年第12期44-47,共4页
Journal of Mechanical Transmission
基金
重庆市科委重点攻关项目(2005CC25)
关键词
故障诊断
互信息熵
支持向量机
无级变速器
Fault diagnosis Mutual information entropy Support vector machine Continuously variable transmission
