Coding Approaches for Fault Detection and Identification in Discrete Event Systems

  • Christoforos N. Hadjicostis
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 660)

Abstract

This chapter applies coding techniques in the context of detecting and identifying faults in complex discrete event systems (DES’s) that can be modeled as Petri nets [Hadjicostis, 1999; Hadjicostis and Verghese, 1999]. The approach is based on replacing the Petri net model of a given DES with a redundant Petri net model in a way that preserves the state, evolution and properties of the original system in some encoded form. This redundant Petri net model enables straightforward fault detection and identification based on simple parity checks that are used to verify the validity of artificially-imposed invariant conditions. Criteria and methods for designing redundant Petri net models that achieve the desired objective while minimizing the cost associated with them (e.g., by minimizing the number of sensors or communication links) are not pursued here, but several examples illustrate how such problems can be approached.

Keywords

Valette 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aghasaryaiu, A., Fabre, E., Benveniste, A., Boubour, R., and Jard, C. (1997a). A Petri net approach to fault detection and diagnosis in distributed systems (Part I). In Proceedings of the 36th IEEE Conf. on Decision and Control, pages 720–725.Google Scholar
  2. Aghasaryaiu, A., Fabre, E., Benveniste, A., Boubour, R., and Jard, C. (1997b). A Petri net approach to fault detection and diagnosis in distributed systems (Part II). In Proceedings of the 36th IEEE Conf. on Decision and Control, pages 726–731.CrossRefGoogle Scholar
  3. Aghasaryaiu, A., Fabre, E., Benveniste, A., Boubour, R., and Jard, C. (1998). Fault detection and diagnosis in distributed systems: an approach by partially stochastic Petri nets. Discrete Event Dynamic Systems: Theory and Applications, 8(2):203–231.MathSciNetCrossRefGoogle Scholar
  4. Baccelli, F., Cohen, G., Olsder, G. J., and Quadrat, J. P. (1992). Synchronization and Linearity. Wiley, New York.MATHGoogle Scholar
  5. Bouloutas, A., Hart, G. W., and Schwartz, M. (1992). Simple finite state fault detectors for communication networks. IEEE Transactions on Communications, 40(3):477–479.CrossRefGoogle Scholar
  6. Cardoso, J., Künzle, L. A., and Valette, R. (1995). Petri net based reasoning for the diagnosis of dynamic discrete event systems. In Proceedings of the IFSA ‘95, the 6th Int. Fuzzy Systems Association World Congress, pages 333–336.Google Scholar
  7. Cassandras, C. G. (1993). Discrete Event Systems. Aksen Associates, Boston.Google Scholar
  8. Cassandras, C. G., Lafortune, S., and Olsder, G. J. (1995). Trends in Control: A European Perspective. Springer-Verlag, London.Google Scholar
  9. Cieslak, R., Desclaux, C., Fawaz, A. S., and Varaiya, P. (1988). Supervisory control of discrete-event processes with partial observations. IEEE Transactions on Automatic Control, 33(3):249–260.CrossRefMATHGoogle Scholar
  10. Debouk, R.,Lafortune, S., and Teneketzis, D. (1998). Coordinated decentralized protocols for failure diagnosis of discrete event systems. In Proceedings of the 37th IEEE Conf. on Decision and Control, pages 3763–3768.Google Scholar
  11. Debouk, R., Lafortune, S., and Teneketzis, D. (1999). On an optimization problem in sensor selection for failure diagnosis. In Proceedings of the 38th IEEE Conf. on Decision and Control, pages 4990–4995.Google Scholar
  12. Debouk, R., Lafortune, S., and Teneketzis, D. (2000). On the effect of communication delays in failure diagnosis of decentralized discrete event systems. In Proceedings of the 39th IEEE Conf. on Decision and Control, pages 2245–2251.Google Scholar
  13. Desrochers, A. A. and Al-Jaar, R. Y. (1994). Applications of Petri Nets in Manufacturing Systems. IEEE Press.Google Scholar
  14. Gertler, J. (1998). Fault Detection and Diagnosis in Engineering Systems. Marcel Dekker, New York.Google Scholar
  15. Hadjicostis, C. N. (1999). Coding Approaches to Fault Tolerance in Dynamic Systems. PhD thesis, EECS Department, Massachusetts Institute of Technology, Cambridge, Massachusetts.Google Scholar
  16. Hadjicostis, C. N. and Verghese, G. C. (1999). Monitoring discrete event systems using Petri net embeddings. In Application and Theory of Petri Nets 1999, number 1639 in Lecture Notes in Computer Science, pages 188–208.Google Scholar
  17. Hadjicostis, C. N. and Verghese, G. C. (2000). Power system monitoring using Petri net embeddings. IEE Proceedings: Generation, Transmission, Distribution, 147(5):299–303.CrossRefGoogle Scholar
  18. Moody, J. O. and Antsaklis, P. J. (1997). Supervisory control using computationally efficient linear techniques: A tutorial introduction. In Proceedings of MED 1997, the 5th IEEE Mediterranean Conf. on Control and Systems.Google Scholar
  19. Moody, J. O. and Antsaklis, P. J. (1998). Supervisory Control of Discrete Event Systems Using Petri Nets. Kluwer Academic Publishers, Boston.CrossRefMATHGoogle Scholar
  20. Moody, J. O. and Antsaklis, P. J. (2000). Petri net supervisors for DES with uncontrollable and unobservable transitions. IEEE Transactions on Automatic Control, 45(3):462–476.MathSciNetCrossRefMATHGoogle Scholar
  21. Murata, T. (1989). Petri nets: Properties, analysis and applications. Proceedings of the IEEE, 77(4):541–580.CrossRefGoogle Scholar
  22. Pandalai, D. N. and Holloway, L. E. (2000). Template languages for fault monitoring of timed discrete event processes. IEEE Transactions on Automatic Control, 45(5):868–882.MathSciNetCrossRefMATHGoogle Scholar
  23. Park, Y. and Chong, E. K. P. (1995). Fault detection and identification in communication networks: a discrete event systems approach. In Proceedings of the 33rd Annual Allerton Conf. on Communication, Control, and Computing, pages 126–135.Google Scholar
  24. Ramadge, P. J. and Wonham, W. M. (1989). The control of discrete event systems. Proceedings of the IEEE, 77(1):81–97.CrossRefGoogle Scholar
  25. Sampath, M., Lafortune, S., and Teneketzis, D. (1998). Active diagnosis of discrete-event systems. IEEE Transactions on Automatic Control, 43(7):908–929.MathSciNetCrossRefMATHGoogle Scholar
  26. Sampath, M., Sengupta, R., Lafortune, S., Sinnamohideen, K., and Teneketzis, D. (1995). Diagnosability of discrete-event systems. IEEE Transactions on Automatic Control, 40(9): 1555–1575.MathSciNetCrossRefMATHGoogle Scholar
  27. Sifakis, J. (1979). Realization of fault-tolerant systems by coding Petri nets. Journal of Design Automation and Fault-Tolerant Computing, 3(2):93–107.MathSciNetGoogle Scholar
  28. Silva, M. and Velilla, S. (1985). Error detection and correction in Petri net models of discrete events control systems. In Proceedings of ISCAS 1985, the IEEE Int. Symp. on Circuits and Systems, pages 921–924.Google Scholar
  29. Tinghuai, C. (1992). Fault diagnosis and fault tolerance: a systematic approach to special topics. Springer-Verlag, Berlin.Google Scholar
  30. Valette, R., Cardoso, J., and Dubois, D. (1989). Monitoring manufacturing systems by means of Petri nets with imprecise markings. In Proceedings of the IEEE Int. Symp. on Intelligent Control, pages 233–238.CrossRefGoogle Scholar
  31. Wang, C. and Schwartz, M. (1993). Fault detection with multiple observers. IEEE/ACM Transactions on Networking, 1(1):48–55.CrossRefGoogle Scholar
  32. Yamalidou, K., Moody, J., Lemmon, M., and Antsaklis, P. (1996). Feedback control of Petri nets based on place invariants. Automatica, 32(1): 15–28.MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • Christoforos N. Hadjicostis
    • 1
  1. 1.Coordinated Science Laboratory and Department of Electrical and Computer EngineeringUniversity of Illinois at Urbana-ChampaignUSA

Personalised recommendations