Model for Unanticipated Fault Detection by OCPCA

摘要：

　　Detection and diagnosis of unanticipated fault has inevitably become a critical issue for PHM (Prognostics and Health Management),especially in the fields of robot,spacecraft and industrial system.It is difficult to overcome this problem since there is lack of history information,prior knowledge and dealing strategy for unanticipated fault.In this paper,a general processing model for unanticipated fault detection and diagnosis is constructed,then,a detection method,named OCPCA (One-class Principal Component Analysis),is proposed.Every OCPCA detector is trained by data from single pattern,and the testing task is to determine whether the testing data is from the very pattern.If the unanticipated fault data is rejected by all OCPCA detectors,then the detection task is accomplished.TEP (Tennessee-Eastman Process),a widely used simulated system based on an actual industrial process,is used to verify the detection of unanticipated fault.The results demonstrate the validity of the proposed model and method.

关键词： Unanticipated Fault Detection and Diagnosis One-class Classification OCPCA

作者: Zhang Ming He Hai Yin Zhou Jiong Qi Wang Yuan Yuan Jiao

作者单位: College of Science,National University of Defense Technology,Changsha 410073,China;Science and Techn College of Science,National University of Defense Technology,Changsha 410073,China;State Key Laborat College of Science,National University of Defense Technology,Changsha 410073,China

会议类型: 国际会议

会议名称: the 2012 International Conference on Manufacturing Engineering and Automation (2012年制造工程与自动化国际会议(ICMEA2012))

会议地点: 广州

会议语种:英文

页码: 2108-2113

在线出版日期: 2012-11-16（万方平台首次上网日期，不代表论文的发表时间）