Diagnosability and online diagnosis of discrete-event systems modeled by acyclic labeled Petri nets

Abstract

We address in this paper the problems of online diagnosis and verification of language diagnosability of discrete event systems (DES) modeled by acyclic labeled Petri nets, in which, different transitions can be labeled by the same event (observable, unobservable and failure). The proposed diagnoser makes its decision regarding the failure occurrence by storing the sequence of observed events and, after each occurrence of observable event, it verifiers if two sets of inequalities are satisfied; the first set accounts for the normal whereas the second one accounts for the faulty behavior of the system. We also consider the problem of diagnosability verification by creating new sets of inequalities that, when satisfied, allow us to decide whether the language generated by the Petri net is diagnosable. Our method for online diagnosis has the advantage over previously ones for relying only on the verification of set of inequalities. Regarding language diagnosability, our verification algorithm does not require any knowledge of automaton theory, being self-contained within the Petri net formalism.