Fault Detection and Diagnosis Based on KPCA-LSSVM Model

摘要：

Although Kernel Principle Component Analysis (KPCA) has been used to monitoring nonlinear processes, it is not well suited for fault diagnosis. In order to solve this problem, a new method of fault detection and diagnosis for nonlinear processes based on KPCA and Least Squares Support Vector Machine(LSSVM) is proposed. The KPCA is used to monitor faults and extract feature and LSSVM model is used to diagnose fault, LSSVM model is constructed based on nonlinear principle component scores of various known faults. The applications in TE process illustrate the efficiency of the proposed approach.

关键词： KPCA LSSVM Fault detecion Fault diagnosis

作者: Zhonghai Li Yan Zhang Liying Jiang

作者单位: College of Automation Shenyang Institute of Aeronautical Engineering Shenyang,China

会议类型: 国际会议

会议名称: 2009 International Conference on Measuring Technology and Mechatronics Automation(ICMTMA 2009)(2009年检测技术与机械自动化国际会议)

会议地点: 长沙

会议语种:英文

页码: 634-638

在线出版日期: 2009-04-11（万方平台首次上网日期，不代表论文的发表时间）