Diagnosing PN-based models with partial observable transitions
Failure diagnosis back-traces failures based on an observed system behaviour when a failure occurs. Ushio et al . (1998) first studied the diagnosability of Petri net (PN) models, a common mathematical modelling structure for CIM systems. With transitions all assumed to be unobservable, the diagnosis process of Ushio et al . relies solely on marking changes at observable places, making diagnosability analysis of most non-trivial PN non-diagnosable. However, in the context of manufacturing process, the initiation of a command and the response of a process are readily available to supervisory controller. This paper, in contrast, assumes that part of the transitions of the PN modelling is observable. Under this assumption, the first contribution of this paper is to investigate how both the label propagation function and the range function, used to construct a diagnoser , are to be revised in order to take advantage of the newly available information provided by observable transitions. The second contribution is to present a procedure to construct, for PN modelling, the associated verifier , first proposed by Yoo and Lafortune (2001) as a polynomial check mechanism on diagnosability but for finite state automata models. As shown by examples, the additional information from observed transitions in general adds diagnosability to the analysed system.
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