skip to main content
research-article

Diagnosability under Weak Fairness

Published:08 December 2015Publication History
Skip Abstract Section

Abstract

In partially observed Petri nets, diagnosis is the task of detecting whether the given sequence of observed labels indicates that some unobservable fault has occurred. Diagnosability is an associated property of the Petri net, stating that in any possible execution, an occurrence of a fault can eventually be diagnosed.

In this article, we consider diagnosability under the weak fairness (WF) assumption, which intuitively states that no transition from a given set can stay enabled forever—it must eventually either fire or be disabled. We show that a previous approach to WF-diagnosability in the literature has a major flaw and present a corrected notion. Moreover, we present an efficient method for verifying WF-diagnosability based on a reduction to LTL-X model checking. An important advantage of this method is that the LTL-X formula is fixed—in particular, the WF assumption does not have to be expressed as a part of it (which would make the formula length proportional to the size of the specification), but rather the ability of existing model checkers to handle weak fairness directly is exploited.

References

  1. A. Agarwal, A. Madalinski, and S. Haar. 2012. Effective verification of weak diagnosability. In Proc. SAFEPROCESS’12. IFAC. DOI:http://dx.doi.org/10.3182/20120829-3-MX-2028.00083Google ScholarGoogle Scholar
  2. S. Biswas. 2013a. Equivalence of fair diagnosability and stochastic diagnosability of discrete event systems. In Proc. IEEE SMC. 378--383. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. S. Biswas. 2013b. Fair diagnosability in PN-based DES models. In Proc. ICCA. 378--383.Google ScholarGoogle Scholar
  4. S. Biswas, D. Sarkar, S. Mudhopadhyay, and A. Patra. 2010. Fairness of transitions in diagnosability analysis of discrete event systems. Discrete Event Dyn. Syst. Theory Appl. 20, 3 (September 2010), 349--376. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. M. P. Cabasino, A. Giua, S. Lafortune, and C. Seatzu. 2012. A new approach for diagnosability analysis of Petri nets using verifier nets. IEEE Trans. Autom. Control 57, 12 (December 2012), 3104--3117.Google ScholarGoogle ScholarCross RefCross Ref
  6. V. Germanos, S. Haar, V. Khomenko, and S. Schwoon. 2014. Diagnosability under weak fairness. In Proc. ACSD’14, A. Mokhov and L. Bernardinello (Eds.). IEEE Computing Society Press, 132--141. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. S. Haar, C. Rodríguez, and S. Schwoon. 2013. Reveal your faults: It’s only fair! In Proc. ACSD’13. IEEE Computer Society Press, 120--129. DOI:http://dx.doi.org/10.1109/ACSD.2013.15 Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. S. Jiang, Z. Huang, V. Chandra, and R. Kumar. 2001. A polynomial algorithm for testing diagnosability of discrete event systems. IEEE Trans. Autom. Control 46, 8, 1318--1321.Google ScholarGoogle ScholarCross RefCross Ref
  9. L. Lamport. 1983. What good is temporal logic? In Proc. IFIP Congr.’83. Elsevier, 657--668.Google ScholarGoogle Scholar
  10. A. Madalinski and V. Khomenko. 2010. Diagnosability verification with parallel LTL-X model checking based on Petri net unfoldings. In Proc. SysTol’10. IEEE Computer Society Press, 398--403.Google ScholarGoogle Scholar
  11. A. Madalinski, F. Nouioua, and P. Dague. 2010. Diagnosability verification with Petri net unfoldings. KES J. 14, 2 (2010), 49--55. DOI:http://dx.doi.org/10.3233/KES-2010-0191 Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. M. Mäkelä. 2005. Maria: The Modular Reachability Analyzer. Retrieved from http://www.tcs.hut.fi/Software/maria/index.en.html.Google ScholarGoogle Scholar
  13. A. Pnueli. 1977. The temporal logic of programs. In Proc. FOCS’77. 46--57. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. M. Sampath, R. Sengupta, S. Lafortune, K. Sinnamohideen, and D. Teneketzis. 1995. Diagnosability of discrete events systems. IEEE Trans. Autom. Control 40, 9 (1995), 1555--1575.Google ScholarGoogle ScholarCross RefCross Ref
  15. A. Schumann and Y. Pencolé. 2007. Scalable diagnosability checking of event-driven systems. In Proc. IJCAI’07. 575--580. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. D. Thorsley and D. Teneketzis. 2005. Diagnosability of Stochastic discrete event systems. IEEE Trans. Autom. Control 50, 4 (2005), 476--492.Google ScholarGoogle ScholarCross RefCross Ref
  17. W. Vogler. 1995. Fairness and partial order semantics. Inf. Process. Lett. 55, 1 (1995), 33--39. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. T.-S. Yoo and S. Lafortune. 2002. Polynomial-time verification of diagnosability of partially observed discrete-event systems. IEEE Trans. Autom. Control 47, 9 (2002), 1491--1495.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Diagnosability under Weak Fairness

            Recommendations

            Comments

            Login options

            Check if you have access through your login credentials or your institution to get full access on this article.

            Sign in

            Full Access

            PDF Format

            View or Download as a PDF file.

            PDF

            eReader

            View online with eReader.

            eReader
            About Cookies On This Site

            We use cookies to ensure that we give you the best experience on our website.

            Learn more

            Got it!