Fault Detection and Isolation Based on Multivariate Statistical Analyzing for the Satellite Attitude Control System

摘要：

Principal Component Analysis (PCA) combining with multivariate statistical knowledge is used for the sensor fault detection and diagnosis according to the characteristics of the satellite attitude control system. In this paper, the principle of PCA to detect faults is presented, and the conventional PCA fault isolation approach is improved. The example of using PCA to fault detection and diagnosis of the typical fault of the infrared earth sensor is given, which is based on faults simulation. The result shows that it is feasible for the fault diagnosis of sensors in the satellite attitude control system and the PCA approach has good performances in fault detection and diagnosis.

关键词： satellite attitude control system principal component analysis (PCA) fault detection and diagnosis multivariate statistical

作者: Lin Su Chaoxuan Shang Yunhong Su Yihua Zhai

作者单位: Department of Optics and Electronics,Ordnance Engineering College,Shijiazhuang,Hebei,Province,050003 Department of Aviation Theory,Aviation University of Air Force,Changchun,Jilin,Province,130022,China School of Management,Harbin Institute of Technology,Harbin,Heilongjiang,Province,150001,China

会议类型: 国际会议

会议名称: 2009 9th International Conference on Electronic Measurement & Instruments(第九届电子测量与仪器国际会议 ICEMI2009)

会议地点: 北京

会议语种:英文

页码: 4114-4117

在线出版日期: 2009-08-16（万方平台首次上网日期，不代表论文的发表时间）