• DocumentCode
    1728520
  • Title

    Sequential probability ratio test based fault detection method for actuators in GNC system

  • Author

    Dan Li ; Miao Zhang ; Zhenzhou Lai ; Yi Shen

  • Author_Institution
    Dept. of Control Sci. & Eng., Harbin Inst. of Technol., Harbin, China
  • fYear
    2013
  • Firstpage
    6324
  • Lastpage
    6327
  • Abstract
    This paper proposes a fault diagnosis method of satellite actuator based on parameters sequential probability ratio test. Sequential probability ratio test is a hypothesis test based on statistical learning, whose greatest advantages is that it does not need to prescribed the number of observed sample group, but compare each hypothesis test data to the set threshold value. Firstly, give the hypothesis testing and drawn threshold based on the given false alarm rate and missed alarm rate. Secondly, extract the characteristic parameters of the signal to be inspected, Finally, calculate the likelihood values and compare to threshold value, so that we can judge whether there is a fault signal. This article made verification experiment based on actuator fault detection of Autonomous GNC system of deep space probes, as can be seen from the simulation results, sequential probability ratio detection method can made a real-time detection of the fault signal. Compared with the fault diagnosis method based on state observer, this method reduces the average inspection time of the fault greatly.
  • Keywords
    actuators; aerospace instrumentation; aerospace testing; fault diagnosis; statistical analysis; autonomous GNC system; deep space probes; fault detection method; fault diagnosis method; real-time detection; satellite actuator; sequential probability ratio test; statistical learning; Actuators; Fault detection; Fault diagnosis; Observers; Probability; Simulation; Testing; GNC system; actuator; fault detection; sequential probability ratio test;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control Conference (CCC), 2013 32nd Chinese
  • Conference_Location
    Xi´an
  • Type

    conf

  • Filename
    6640546