Fault detection of oil pump based on classify support vector machine

被引：0

作者：

Tian, Jingwen ^{[1
]}

Gao, Meijuan ^{[1
]}

Li, Kai ^{[2
]}

Zhou, Hao ^{[2
]}

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

来源：

2007 IEEE INTERNATIONAL CONFERENCE ON CONTROL AND AUTOMATION, VOLS 1-7 | 2007年

关键词：

statistical learning theory; support vector machine; oil pump; fault detection;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

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.

引用

页码：3040 / +

页数：2