Diagnosis and Automata

Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 433)

Abstract

Fault diagnosis and state estimation are two central and typical problems one may face in the monitoring of discrete-event systems. This chapter examines these two problems in the simple setting of automata. It is first explained that diagnosis and state estimation are two related problems. Then one describes the construction of an observer (resp. a diagnoser) both for standard and for probabilistic automata. A section is dedicated to diagnosability issues, that is the ability to detect the occurrence of an unobservable fault event after a bounded number of observations following that fault. The chapter then proposes an opening to the case of distributed systems, made of several interacting components, but still assuming a sequential semantics (i.e. ignoring the possible parallelism of some events). One first presents a modularity property on observers and diagnosers of distributed systems, in a rather specific case. The general case is then examined, and a distributed procedure is described to recover the runs of a distributed system that can explain a set of distributed observations collected in this system. Finally, the chapter closes on a discussion about the interest of true concurrency semantics for the monitoring of large distributed systems.

Keywords

Entropy Agate Expense 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Benveniste, A., Fabre, E., Jard, C., Haar, S.: Diagnosis of asynchronous discrete event systems a net unfolding approach. IEEE Transactions on Automatic Control 48(5), 714–727 (2003)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Buchsbaum, A.L., Giancarlo, R., Westbrook, J.R.: On the determinization of weighted finite automata. SIAM Journal on Computing 30, 1502–1531 (1998)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Cassandras, C.G., Lafortune, S.: Introduction to Discrete Event Systems, 2nd edn. Springer (2008)Google Scholar
  4. 4.
    Cortes, C., Mohri, M., Rastogi, A., Riley, M.D.: Efficient Computation of the Relative Entropy of Probabilistic Automata. In: Correa, J.R., Hevia, A., Kiwi, M. (eds.) LATIN 2006. LNCS, vol. 3887, pp. 323–336. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Fabre, E., Benveniste, A., Haar, S., Jard, C.: Distributed monitoring of concurrent and asynchronous Systems. Journal of Discrete Event Systems 15(1), 33–84 (2005)MathSciNetMATHCrossRefGoogle Scholar
  6. 6.
    Fabre, E., Benveniste, A.: Partial order techniques for distributed discrete event systems: why you can’t avoid using them. Journal of Discrete Events Dynamical Systems 17(3), 355–403 (2007)MathSciNetMATHCrossRefGoogle Scholar
  7. 7.
    Fabre, E., Jezequel, L.: Distributed optimal planning: an approach by weighted automata calculus. In: Proc. 48th Conference on Decision and Control, Shangai, China (2009)Google Scholar
  8. 8.
    Fabre, E., Jezequel, L.: On the construction of probabilistic diagnosers. In: Proc. 10th Workshop on Discrete Event Systems, Berlin, Germany (2010)Google Scholar
  9. 9.
    Jeron, T., Marchand, H., Pinchinat, S., Cordier, M.O.: Supervision patterns in discrete event systems diagnosis. In: Proc. 8th Workshop on Discrete Event Systems, Ann Arbor, Michigan (2006)Google Scholar
  10. 10.
    Kirsten, D., Murer, I.: On the determinization of weighted automata. Journal of Automata, Languages and Combinatorics 10(2/3), 287–312 (2005)MathSciNetMATHGoogle Scholar
  11. 11.
    Mohri, M.: Weighted automata algorithms. In: Kuich, W., Vogler, H., Droste, M. (eds.) Handbook of Weighted Automata. Springer (2009)Google Scholar
  12. 12.
    Mohri, M.: Finite-state transducers in language and speech processing. Computational Linguistics 23, 269–311 (1997)MathSciNetGoogle Scholar
  13. 13.
    Mohri, M.: Generic epsilon-removal and input epsilon-renormalization algorithms for weighted transducers. International Journal on Foundations of Computer Sciences 13(1), 129–143 (2002)MathSciNetMATHCrossRefGoogle Scholar
  14. 14.
    Paz, A.: Introduction to Probabilistic Automata. Academic Press, New-York (1971)MATHGoogle Scholar
  15. 15.
    Sampath, M., Sengupta, R., Lafortune, S., Sinnamohideen, K., Teneketzis, D.: Diagnosability of discrete-event systems. IEEE Transactions on Automatic Control 40(9), 1555–1575 (1995)MathSciNetMATHCrossRefGoogle Scholar
  16. 16.
    Thorsley, D., Teneketzis, D.: Diagnosability of stochastic discrete-event systems. IEEE Transactions on Automatic Control 50(4), 476–492 (2005)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Thorsley, D., Yoo, T.S., Garcia, H.E.: Diagnosability of stochastic discrete-event systems under unreliable observations. In: Proc. American Control Conference, Seattle, USA (2008)Google Scholar
  18. 18.
    Ye, L., Dague, P.: An optimized algorithm for diagnosability of component-based systems. In: Proc. 10th Workshop on Discrete Event Systems, Berlin, Germany (2010)Google Scholar
  19. 19.
    Yoo, T.S., Lafortune, S.: Polynomial-time verification of diagnosability of partially observed discrete-event systems. IEEE Transactions on Automatic Control 47(9), 1491–1495 (2002)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Zhou, C., Kumar, R., Sreenivas, R.S.: Decentralized modular diagnosis of concurrent discrete event systems. In: 9th Workshop on Discrete Event Systems, Goteborg, Sweden (2008)Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.INRIA Rennes Bretagne AtlantiqueRennes cedexFrance

Personalised recommendations