基于自适应助推算法的集成支持向量机在柴油机故障诊断中的应用  

The Application of Ensemble Support Vector Machine to Fault Diagnosis of Diesel Engine Based on AdaBoost Algorithm

作　　者：王自营[1] 邱绵浩[1] 安钢[1] 王凯[1] 

机构地区：[1]装甲兵工程学院机械工程系,北京100072

出　　处：《兵工学报》2009年第10期1368-1374,共7页Acta Armamentarii

摘　　要：利用支持向量机(SVM)进行机械故障诊断时,分类效果与核函数紧密相关。但核函数的选取一直缺少明确的理论指导,而且由于学习过程中常采取近似计算,致使分类结果远非期望水平。本研究首先利用匀幅、互信息指标构造特征向量;而后基于自适应助推法得到一系列基本SVM;并基于多样性准则对这些基本SVM进行筛选,最后对满足条件的基本SVM加权得到集成SVM。将集成SVM应用到某型坦克柴油机的故障诊断中,性能评价及分类结果表明,集成SVM比单一SVM具有更好的分类性能,故障诊断准确率更高。When mechanical fault is diagnosed by support vector machine （SVM）, the classification effect is closely related to the kernel function. As selecting of the kernel function always lacks theoretical guidance, and approximate computation is adopted in learning course, it led to that the classification result is far from being the expected level. The eigenvector was constructed by mutual information and even amplitude indexes; a series of basic SVM was got by AdaBoost; the ensemble SVM was got by screening the basic SVM with rule of diversity, and weighting the basic SVM which satisfied the diversity requirement. After the ensemble SVM is put into fault diagnosis of diesel engine of tank, the evaluation of performance and classification results denote that the ensemble SVM has better classification performance and a higher classification success rate than single SVM.

关 键 词：信息处理技术 自适应 支持向量机 故障诊断 

分 类 号：TH17[机械工程—机械制造及自动化] TP18[自动化与计算机技术—控制理论与控制工程]