Title :
Fault Detection of Oil Pump Based on Classify Support Vector Machine
Author :
Tian, Jingwen ; Gao, Meijuan ; Li, Kai ; Zhou, Hao
Author_Institution :
Beijing Union Univ., Beijing
fDate :
May 30 2007-June 1 2007
Abstract :
Statistical learning theory is introduced to fault detection of oil 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.
Keywords :
fault diagnosis; genetic algorithms; nonlinear functions; pumps; self-adjusting systems; support vector machines; fault detection; fault information; genetic algorithms; global optimization; nonlinear function approach; oil pump; self-learning ability; statistical learning theory; support vector machine; Artificial neural networks; Automatic control; Costs; Fault detection; Petroleum; Production; Pumps; Statistical learning; Support vector machine classification; Support vector machines; fault detection; oilpump; statistical learning theory; support vector machine;
Conference_Titel :
Control and Automation, 2007. ICCA 2007. IEEE International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
978-1-4244-0818-4
Electronic_ISBN :
978-1-4244-0818-4
DOI :
10.1109/ICCA.2007.4376416