A survey on efficient diagnosability tests for automata and bounded Petri nets

This paper presents a survey and evaluation of the efficiency of polynomial diagnosability algorithms for systems modeled by Petri nets and automata. A modified verification algorithm that reduces the state space by exploiting symmetry and abstracting unobservable transitions is also proposed. We show the importance of minimal explanations on the performance of diagnosability verifiers. Different verifiers are compared in terms of state space and elapsed time. It is shown that the minimal explanation notion involved in the modified basis reachability graph, a graph presented by Cabasino et al. [3] for diagnosability analysis of Petri nets, has great impact also on automata-based diagnosability methods. The evaluation often shows improved computation times of a factor 1000 or more when the concept of minimal explanation is included in the computation.

[1]  Behzad Bordbar,et al.  On-Line Monitoring of Large Petri Net Models Under Partial Observation , 2008, Discret. Event Dyn. Syst..

[2]  George Jiroveanu,et al.  The Diagnosability of Petri Net Models Using Minimal Explanations , 2010, IEEE Transactions on Automatic Control.

[3]  Alessandro Giua,et al.  Diagnosability of bounded Petri nets , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[4]  Raja Sengupta,et al.  Diagnosability of discrete-event systems , 1995, IEEE Trans. Autom. Control..

[5]  Alessandro Giua,et al.  Fault detection for discrete event systems using Petri nets with unobservable transitions , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[6]  Stéphane Lafortune,et al.  Polynomial-time verification of diagnosability of partially observed discrete-event systems , 2002, IEEE Trans. Autom. Control..

[7]  Shengbing Jiang,et al.  A polynomial algorithm for testing diagnosability of discrete-event systems , 2001, IEEE Trans. Autom. Control..

[8]  João Carlos Basilio,et al.  Polynomial Time Verification of Decentralized Diagnosability of Discrete Event Systems , 2011, IEEE Trans. Autom. Control..

[9]  Tae Sic Yoo,et al.  Accepted for publication in Transactions of Automatic Control Polynomial Time Veri cation of Diagnosability of Partially Observed Discrete Event Systems , 2006 .

[10]  Christos G. Cassandras,et al.  Introduction to Discrete Event Systems , 1999, The Kluwer International Series on Discrete Event Dynamic Systems.

[11]  René Boel,et al.  Distributed contextual diagnosis for very large systems , 2004 .