• DocumentCode
    2941244
  • 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
  • Volume
    1
  • fYear
    2009
  • fDate
    11-12 April 2009
  • Firstpage
    654
  • Lastpage
    657
  • 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;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Measuring Technology and Mechatronics Automation, 2009. ICMTMA '09. International Conference on
  • Conference_Location
    Zhangjiajie, Hunan
  • Print_ISBN
    978-0-7695-3583-8
  • Type

    conf

  • DOI
    10.1109/ICMTMA.2009.582
  • Filename
    5203057