Title :
Fault Diagnosis of Satellite Based on SVM Observer
Author :
Zhao Shi-lei ; Zhang Yin-Chun
Author_Institution :
Res. Center of Satellite Technol., Harbin Inst. of Technol., Harbin, China
Abstract :
Towards an general unknown actuator fault of the satellite attitude control system, this paper propose a new method based on SVM observer which use two LS-SVM regression models to identify the unknown fault and the nonlinear term respectively, because the training datasets of an unknown fault cannot be acquired before it occurred, we combine offline training with online incremental learning method to reduce the approximation error. In the last part, this method is applied to detect a kind of actuator fault, the simulation shows that the method proposed by this paper is effective.
Keywords :
actuators; artificial satellites; control engineering computing; fault diagnosis; learning (artificial intelligence); regression analysis; support vector machines; LS-SVM regression models; SVM observer; actuator fault; fault diagnosis; incremental learning method; satellite attitude control system; Actuators; Automation; Fault detection; Fault diagnosis; Isolation technology; Mechatronics; Neural networks; Redundancy; Satellites; Support vector machines; LS-SVM; SACS; incremental learning;
Conference_Titel :
Measuring Technology and Mechatronics Automation, 2009. ICMTMA '09. International Conference on
Conference_Location :
Zhangjiajie, Hunan
Print_ISBN :
978-0-7695-3583-8
DOI :
10.1109/ICMTMA.2009.582