基于混沌特征和支持向量机的内燃机故障诊断  

FAULT DIAGNOSIS OF ENGINE BASED ON CHAOTIC FEATURES AND SUPPORT VECTOR MACHINE

作　　者：吴震宇[1] 袁惠群[2] 李玲[1] 

机构地区：[1]武汉船用机械有限责任公司技术中心,武汉430084 [2]东北大学机械工程与自动化学院,沈阳110004

出　　处：《机械强度》2010年第5期723-728,共6页Journal of Mechanical Strength

基　　金：国家863高技术基金(2006AA04Z408)资助项目~~

摘　　要：为解决内燃机故障诊断这一复杂问题,对6110型柴油机进行气门间隙故障模拟试验,测得振动信号,并计算其关联维数、最大Lyapunov指数、Kolmogorov熵3个混沌特征和它们的统计特征,作为故障特征量,利用支持向量机对故障进行识别。结果表明,用单一的混沌特征识别故障类型,效果较差;将统计特征与混沌特征共同作为故障的特征向量,效果较好。In order to resolve the complicated problems of the engine fault diagnosis,the vibration signals of the engine were obtained by simulating the engine valve faults,and meanwhile 3 chaotic features such as the correlation dimensions,maximum Lyapunov exponent,Kolmogorov entropy and their statistical features were obtained as the fault features.Different fault patterns were identified using support vector machine.The results indicate that the classification performance of the single chaotic feature is more poor...

关 键 词：内燃机故障诊断 混沌特征 支持向量机 时域统计特征 

分 类 号：TK407[动力工程及工程热物理—动力机械及工程]