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Fault model identification with Petri nets

Authors: Cabasino, M. P.; Giua, A.; Hadjicostis, Christoforos N.; Seatzu, C.; Cabasino, M. P.; Giua, A.; Hadjicostis, Christoforos N.; +1 Authors

Fault model identification with Petri nets

Abstract

Most of the fault identification problems in the discrete event systems literature assume knowledge of the structure of the net system, including the nature (and behavior) of the possible faults. In this paper we deal with this problem within the framework of Petri nets by removing the requirement that the nature (and behavior) of the fault is known. In particular, we devise a way to identify the structure of the faulty transitions of the system given its language. Then, we generalize this procedure to unobservable faults, in which case the structure of the faulty system needs to be recognized from the knowledge of the structure of the fault-free system, and the projection of the faulty system language on the set of non-faulty events, that are assumed to be observable.

Countries
Italy, Cyprus
Keywords

Marine biology, Net systems, Discrete event systems, Linguistics, Petri nets, Fault identifications, Unobservable, Graph theory, Electric fault location, Faulty transitions, Fault modeling, Technical presentations

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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Average
Top 10%
Average
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