# Diagnosability analysis of patterns on bounded labeled prioritized Petri nets

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## Abstract

Checking the diagnosability of a discrete event system aims at determining whether a fault can always be identified with certainty after the observation of a bounded number of events. This paper investigates the problem of pattern diagnosability of systems modeled as bounded labeled prioritized Petri nets that extends the diagnosability problem on single fault events to more complex behaviors. An effective method to automatically analyze the diagnosability of a pattern is proposed. It relies on a specific Petri net product that turns the pattern diagnosability problem into a model-checking problem.

## Keywords

Fault diagnosis Diagnosability Pattern Petri nets## References

- Basile F, Chiacchio P, De Tommasi G (2012) On K-diagnosability of Petri nets via integer linear programming. Automatica 48(9):2047–2058MathSciNetCrossRefMATHGoogle Scholar
- Benveniste A, Fabre E, Haar S, Jard C (2003) Diagnosis of asynchronous discrete-event systems: a net unfolding approach. Trans Autom Control 48(5):714–727MathSciNetCrossRefGoogle Scholar
- Berthomieu B, Ribet P O, Vernadat F (2004) The tool tina – construction of abstract state spaces for Petri nets and time Petri nets. Int J Prod Res 42(14):2741–2756CrossRefMATHGoogle Scholar
- Berthomieu B, Peres F, Vernadat F (2006) Bridging the gap between timed automata and bounded time Petri nets. In: 4th International conference formal modeling and analysis of timed systems. Paris, pp 82– 97Google Scholar
- Berthomieu B, Peres F, Vernadat F (2007) Model-checking bounded prioriterized time Petri nets. In: Automated technology for verification and analysis, vol 4762. Springer Verlag, LNCS, pp 523– 532Google Scholar
- Cabasino MP, Giua A, Seatzu C (2009) Diagnosability of bounded Petri nets. In: Proceedings of the 48th IEEE conference on decision and control, 2009 held jointly with the 2009 28th chinese control conference. CDC/CCC 2009. IEEE, pp 1254–1260Google Scholar
- Cabasino M P, Giua A, Seatzu C (2014) Diagnosability of discrete-event systems using labeled Petri nets. IEEE Trans Autom Sci Eng 11(1):144–153CrossRefGoogle Scholar
- Cimatti A, Pecheur C, Cavada R (2003) Formal verification of diagnosability via symbolic model checking. In: 18th International joint conference on artificial intelligence. Acapulco, pp 363–369Google Scholar
- Clarke E M, Grumberg O, Peled DA (1999) Model checking. MIT pressGoogle Scholar
- Genc S, Lafortune S (2007) Distributed diagnosis of place-bordered Petri nets. IEEE Trans Autom Sci Eng 4(2):206–219CrossRefGoogle Scholar
- Giua A (2007) A benchmark for diagnosis. http://www.diee.unica.it/giua/WODES/WODES08/media/benchmark_diagnosis.pdf
- Giua A, Seatzu C (2005) Fault detection for discrete event systems using Petri nets with unobservable transitions. In: 44th IEEE Conference on decision and control, 2005 and 2005 European control conference. Seville, pp 6323–6328Google Scholar
- Gougam H E, Subias A, Pencolé Y (2013) Supervision patterns: formal diagnosability checking by Petri net unfolding. In: 4th IFAC Workshop on dependable control of discrete systems. York, pp 73–78Google Scholar
- Grastien A (2009) Symbolic testing of diagnosability. In: International workshop on principles of diagnosis (DX-09), pp 131–138Google Scholar
- Haar S, Benveniste A, Fabre E, Jard C (2003) Partial order diagnosability of discrete event systems using Petri net unfoldings. In: 42nd IEEE Conference on decision and control. Maui, pp 3748– 3753Google Scholar
- Hack M (1975) Petri net languages. Tech. Rep. 124, M.I.T. Project MAC, Computation Structures Group, Massachusetts Institute of TechnologyGoogle Scholar
- Jéron T, Marchand H, Pinchinat S, Cordier MO (2006) Supervision patterns in discrete event systems diagnosis. In: 8th International workshop on discrete event systems. Ann Arbor, pp 262–268Google Scholar
- Jiang S, Kumar R (2004) Failure diagnosis of discrete-event systems with linear-time temporal logic specifications. Trans Autom Control 49(6):934–945MathSciNetCrossRefGoogle Scholar
- Jiang S, Huang Z, Chandra V, Kumar R (2001) A polynomial algorithm for testing diagnosability of discrete-event systems. Trans Autom Control 46(8):1318–1321MathSciNetCrossRefMATHGoogle Scholar
- Lai S, Nessi D, Cabasino M P, Giua A, Seatzu C (2008) A comparison between two diagnostic tools based on automata and Petri nets. In: 9th International workshop on discrete event systems. Göteborg, pp 144–149Google Scholar
- Lamperti G, Zanella M (2006) Flexible diagnosis of discrete-event systems by similarity-based reasoning techniques. Artif Intell 170(3):232–297MathSciNetCrossRefMATHGoogle Scholar
- Lefebvre D, Delherm C (2007) Diagnosis of DES with Petri net models. IEEE Trans Autom Sci Eng 4(1):114–118CrossRefGoogle Scholar
- Lin F (1994) Diagnosability of discrete event systems and its applications. J Discret Event Dyn Syst Theory Appl 4(2):197–212CrossRefMATHGoogle Scholar
- Liu B (2014) An efficient approach for diagnosability and diagnosis of des based on labeled Petri nets - untimed and timed contexts. PhD thesis, Univ. Lille NordGoogle Scholar
- Liu B, Ghazel M, Toguyéni A (2014a) OF-PENDA: a software tool for fault diagnosis of discrete event systems modeled by labeled Petri nets. In: Proceedings of the 1st international workshop on Petri nets for adaptive discrete-event control systems (ADECS 2014), no. 1161 in CEUR Workshop Proceedings, pp 20–35Google Scholar
- Liu B, Ghazel M, Toguyéni A (2014b) Toward an efficient approach for diagnosability analysis of DES modeled by labeled Petri nets. In: 13th European control conference. Strasbourg, pp 1293– 1298Google Scholar
- Pencolé Y (2004) Diagnosability analysis of distributed discrete event systems. In: 16th European conference on artificial intelligence. Valencia, pp 43–47Google Scholar
- Pencolé Y, Cordier M O (2005) A formal framework for the decentralised diagnosis of large scale discrete event systems and its application to telecommunication networks. Artif Intell 164(2):121–170MathSciNetCrossRefMATHGoogle Scholar
- Pencolé Y, Schumann A, Kamenetsky D (2006) Towards low-cost fault diagnosis in large component-based systems. In: 6th IFAC Symposium on fault detection, supervision and safety of technical processes. Beijing, pp 1473–1478Google Scholar
- Peterson J L (1977) Petri nets. ACM Comput Surv 9(3):223–252MathSciNetCrossRefMATHGoogle Scholar
- Rozé L, Cordier M O (2002) Diagnosing discrete-event systems: extending the diagnoser approach to deal with telecommunication networks. J Discret-Event Dyn Syst Theory Appl (JDEDS) 12(1):43–81MathSciNetCrossRefMATHGoogle Scholar
- Sampath M, Sengupta R, Lafortune S, Sinnamohideen K, Teneketzis D (1995) Diagnosability of discrete-event systems. Trans Autom Control 40(9):1555–1575MathSciNetCrossRefMATHGoogle Scholar
- Schnoebelen P (2003) The complexity of temporal logic model checking. Adv Modal Logic 4:393–436MathSciNetMATHGoogle Scholar
- Schumann A, Pencolé Y (2007) Scalable diagnosability checking of event-driven systems. In: 20th International joint conference on artificial intelligence. Hyderabad, pp 575–580Google Scholar
- Ye L, Dague P (2012) A general algorithm for pattern diagnosability of distributed discrete event systems. In: Proceedings of the 24th IEEE international conference on tools with artificial intelligence (ICTAI 2012), pp 130–137Google Scholar
- Yoo T S, Lafortune S (2002) Polynomial-time verification of diagnosability of partially observed discrete-event systems. Trans Autom Control 47(9):1491–1495MathSciNetCrossRefGoogle Scholar
- Zanella M, Lamperti G (2004) Diagnosis of discrete-event systems by separation of concerns, knowledge compilation, and reuse. In: Proceedings of the 16th European conference on artificial intelligence (ECAI04), pp 838–842Google Scholar
- Zaytoon J, Lafortune S (2013) Overview of fault diagnosis methods for discrete event systems. Ann Rev Control 37:308–320CrossRefGoogle Scholar

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