Diagnosability testing with satisfiability algorithms
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Scalable diagnosability checking of event-driven systems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Formal verification of diagnosability via symbolic model checking
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Observability Checking to Enhance Diagnosis of Real Time Electronic Systems
DS-RT '08 Proceedings of the 2008 12th IEEE/ACM International Symposium on Distributed Simulation and Real-Time Applications
Diagnosability verification with Petri net unfoldings
International Journal of Knowledge-based and Intelligent Engineering Systems
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This paper shows that we can take advantage of information about the probabilities of the occurrences of events, when this information is available, to refine the classical results of diagnosability: instead of giving a binary answer, the approach we propose allows one to quantify, in particular, the degree of non-diagnosability in case of negative answer. The dynamics of the system is modelled by a reducible Markov chain. A state of this chain contains information about whether it is faulty (resp. ambiguous) or not. The useful refinements of the decision about diagnosability are then obtained from the asymptotic analysis of this Markov chain. This analysis may be very useful in practice since it may lead to take the decision of tolerating some non-diagnosable systems, if their non-diagnosability is not critical, and thus allows one saving the cost of additional sensors necessary to make these systems diagnosable This work is part of DIAFORE project supported by ANR under grant ANR-05-PDIT-016-05.