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Hybrid Systems Diagnosis

  • Sheila McIlraith
  • Gautam Biswas
  • Dan Clancy
  • Vineet Gupta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1790)

Abstract

This paper reports on an on-going project to investigate techniques to diagnose complex dynamical systems that are modeled as hybrid systems. In particular, we examine continuous systems with embedded supervisory controllers that experience abrupt, partial or full failure of component devices. We cast the diagnosis problem as a model selection problem. To reduce the space of potential models under consideration, we exploit techniques from qualitative reasoning to conjecture an initial set of qualitative candidate diagnoses, which induce a smaller set of models. We refine these diagnoses using parameter estimation and model fitting techniques. As a motivating case study, we have examined the problem of diagnosing NASA’s Sprint AERCam, a small spherical robotic camera unit with 12 thrusters that enable both linear and rotational motion.

Keywords

Hybrid System Candidate Model Continuous System Fault Mode Mode Sequence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Sheila McIlraith
    • 1
  • Gautam Biswas
    • 2
  • Dan Clancy
    • 3
  • Vineet Gupta
    • 3
  1. 1.Knowledge Systems LabStanford UniversityStanford
  2. 2.Computer Science DepartmentVanderbilt UniversityNashville
  3. 3.Caelum Research CorporationNASA Ames Research CenterMoffett Field

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