Application of SVM to Engine Parameter Collector Fault Diagnosis

摘要：

Support Vector Machine (SVM), based on structural risk minimization principle,is now widely used in pattern recognition,classification and other research fields1,2.It shows better generalization performance than traditional statistical learning theory,especially in small samples.In this paper,some dimensionless parameter is selected as SVM eigenvector,and then support vector machine is applied to fault diagnosis in engine parameter collector. Result shows that it has good ability in fault pattern classification of engine parameter collector.

作者: QIN Bo CHEN Ming ZHANG Hao

作者单位: College of Automation,Northwestern Polytechnical University,Xian 710072,China

会议类型: 国际会议

会议名称: The 2nd International Symposium on Systems and Control in Aeronautics and Astronautics(第二届航空航天系统与控制国际会议 ISSCAA 2008)

会议地点: 深圳

会议语种:英文

页码: 647-650

在线出版日期: 2008-12-10（万方平台首次上网日期，不代表论文的发表时间）