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)


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.


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    L. Alenius and V. Gupta. Modeling an AERCam: A case study in modeling with concurrent constraint languages. In Proceedings of the CP’97 Workshop on Modeling and Computation in the Concurrent Constraint Languages, 1998.Google Scholar
  2. 2.
    V. I. Arnold. Mathematical Methods of Classical Mechanics. Springer Verlag, 1978.Google Scholar
  3. 3.
    P. Baroni, G. Lamperti, P. Pogliano and M. Zanella Diagnosis of large active systems Artificial Intelligence, 110(1):135–183, 1999.zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    M. Basseville. On-board component fault detection and isolation using a statistical local approach. Automatica, vol. 34, no. 11, 1998.Google Scholar
  5. 5.
    M. Basseville and I.V. Nikiforov. Detection of Abrupt Changes: Theory and Applications. Prentice Hall, Englewood Cliffs, NJ, 1993.Google Scholar
  6. 6.
    J. A. Blimes. A gentle tutorial of the EM algorithm and its application to parameter estimation for gaussian mixture and hidden markov models. Technical Report TR-97-021, International Computer Science Institute (ICSI) and Computer Science Division, Dept. of Electrical Engineering and Computer Science, U.C. Berkeley, 1998.Google Scholar
  7. 7.
    M. Branicky. Studies in Hybrid Systems: Modeling, Analysis, and Control. PhD thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 1995.Google Scholar
  8. 8.
    A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data. Journal of the Royal Statistical Society Ser. B, 39:1–38, 1977.zbMATHMathSciNetGoogle Scholar
  9. 9.
    B. Etkin and L. D. Reid. Dynamics of Flight:Stability and Control. John Wiley and Sons, 1995.Google Scholar
  10. 10.
    E. Fabre, A. Aghasaryan, A. Benveniste, R. Boubour and C. Jard. Fault detection and diagnosis in distributed systems: an approach by partially stochastic Petri nets. Journal of Discrete Event Dynamic Systems, vol. 8, no. 2, pp. 203–231, 1998.zbMATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    P.M. Frank. Fault diagnosis in dynamic systems using analytic and knowledge-based redundancy: a survey and some new results. Automatica, vol. 26, pp. 459–474, 1990.zbMATHCrossRefGoogle Scholar
  12. 12.
    E.A. Garcia and P.M. Frank. Deterministic nonlinear observer-based approaches to fault diagnosis: a survey. Control Engineering Practice, 5(5):663–670, 1999.CrossRefGoogle Scholar
  13. 13.
    J.J. Gertler. Fault Detection and Diagnosis in Engineering Systems. Marcel Dekker, New York, 1988.Google Scholar
  14. 14.
    W. Hamscher, L. Console and J. de Kleer Readings in Model-based Diagnosis. Morgan Kaufmann, 1992.Google Scholar
  15. 15.
    J. Lunze. A timed discrete-event abstraction of continuous-variable systems. Intl. Jour. of Control, vol. 72, no. 13, pp. 1147–1164, 1999.zbMATHCrossRefMathSciNetGoogle Scholar
  16. 16.
    E.J. Manders, P.J. Mosterman, and G. Biswas. Signal to symbol transformation techniques for robust diagnosis in transcend. In 10th Int. Workshop on Principles of Diagnosis, pp. 155–165, 1999.Google Scholar
  17. 17.
    S. McIlraith. Explanatory diagnosis: Conjecturing actions to explain observations. In Proceedings of the Sixth International Conference on Principles of Knowledge Representation and Reasoning (KR’98), pp. 167–177, 1998.Google Scholar
  18. 18.
    P. Mosterman and G. Biswas. Diagnosis of continuous valued systems in transient operating regions. IEEE Transactions on Systems, Man, and Cybernetics, 1999. vol. 29, no. 6, pp. 554–565, 1999.CrossRefGoogle Scholar
  19. 19.
    P. Mosterman and G. Biswas. Building hybrid observers for complex dynamic systems using model abstractions. In International Workshop on Hybrid Systems: Computation and Control, Nijmegen, Netherlands, March 1999.Google Scholar
  20. 20.
    R.J. Patton and J. Chen. Observer-based fault detection and isolation: robustness and applications. Control Engineering Practice, 5(5):671–682, 1997.CrossRefGoogle Scholar
  21. 21.
    B. Rinner and B. Kuipers. Monitoring piecewise continuous behavior by refining trackers and models. In Hybrid Systems and AI:Modeling, Analysis and Control of Discrete + Continuous Systems, AAAI Technical Report SS-99-05, pp. 164–169, 1999.Google Scholar
  22. 22.
    M. Sampath, R. Sengupta, S. Lafortune, K. Sinnamohideen and D. Teneketzis. Failure diagnosis using discrete-event models. IEEE Trans. on Control Systems Technology, vol. 4, no. 2, pp. 105–124, 1996.CrossRefGoogle Scholar
  23. 23.
    W. Sweet. The glass cockpit. IEEE Spectrum, pages 30–38, September 1995.Google Scholar
  24. 24.
    Y. Weiss. Motion segmentation using EM — a short tutorial., 1997.Google Scholar
  25. 25.
    B. Williams and P.P. Nayak. A model-based approach to reactive self-configuring systems. In Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), pages 971–978, 1996.Google Scholar

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

Personalised recommendations