文献类型：会议论文

英文题名：Fault detection of oil pump based on classify support vector machine

作者：Tian, Jingwen[1];Gao, Meijuan[1];Li, Kai[2];Zhou, Hao[2]

通讯作者：Tian, JW[1]

机构：[1]Beijing Union Univ, Dept Automat Control, Beijing, Peoples R China;[2]Beijing Univ Chem Technol, Sch Informat Sci, Beijing, Peoples R China

第一机构：北京联合大学城市轨道交通与物流学院

通讯机构：[1]corresponding author), Beijing Union Univ, Dept Automat Control, Beijing, Peoples R China.|[1141751]北京联合大学城市轨道交通与物流学院;[11417]北京联合大学;

会议论文集：IEEE International Conference on Control and Automation

会议日期：MAY 30-JUN 01, 2007

会议地点：Guangzhou, PEOPLES R CHINA

语种：英文

外文关键词：statistical learning theory; support vector machine; oil pump; fault detection

摘要：Statistical learning theory is introduced to fault detection of off pump. Considering the issues that the relationship between the fault of oil pump existent and fault information is a complicated and nonlinear system, and it is very difficult to found the process model to describe it The support vector machine (SVM) has the ability of strong nonlinear function approach and the ability of strong generalization and also has the feature of global optimization. In this paper, a fault detection method of oil pump based on SVM is presented, moreover, the genetic algorithm(GA) was used to optimize SVM parameters. With the ability of strong self-learning and well generalization of SVM, the detection method can truly diagnosticate the fault of oil pump by learning the fault information of oil pump. The real detection results show that this method is feasible and effective.