基于废气成分分析和支持向量机的发动机故障诊断  

Fault diagnosis of engine based on exhaust density analysis and support vector machines

作　　者：李增芳[1] 金春华[2] 何勇[3] 

机构地区：[1]浙江水利水电高等专科学校机电工程系,杭州310018 [2]宁波大学生命科学与生物工程学院,宁波315211 [3]浙江大学生物系统工程与食品科学学院,杭州310029

出　　处：《农业工程学报》2010年第4期143-146,共4页Transactions of the Chinese Society of Agricultural Engineering

基　　金：高等学校优秀青年教师教学科研奖励计划资助项目(02411);浙江省教育厅科研计划项目(20070850)

摘　　要：为了实现发动机故障的快速实时诊断,提出了一种基于废气成分分析和支持向量机的发动机故障诊断方法。该方法首先运用NHA500废气分析仪采集发动机典型故障状态下的HC、CO、CO2、O2、NOX等废气参数值,接着对采集到的数据进行规范化处理,提取特征向量作为学习样本,然后用于设计训练基于支持向量机的多元分类器,进行故障类型识别。试验结果表明,采用纠错编码的支持向量机分类方法比神经网络具有更好的抗干扰性和更强的分类能力,在小样本的情况下故障诊断正确率达98.5%,能有效描述废气成分变化和故障状态之间的复杂关系。In order to realize real-time fault diagnosis,a method for engine fault diagnosis based on exhaust density analysis and support vector machines（SVM）was put forward.Under typical fault working conditions of the engine, firstly,the data of exhaust densities of HC,CO,CO2,O2,NOXwere gotten by using NHA-500 exhaust density analysis instrument.Then the data were normalized,and feature vectors were extracted from the data as learning samples and then used in designing and training multielement classifier based on support vector machines for fault pattern recognition. Experimental results showed that error correction coding classification method based on support vector machines was better in classification ability and had stronger anti-jamming capability than neural networks.In the case of small samples,accuracy rate of this fault diagnostic method could reach 98.5%.The result means that the method can effectively describe the complex relationship between exhaust compents changes and fault states.

关 键 词：发动机 故障检测 支持向量机 废气 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TK418[自动化与计算机技术—控制科学与工程]