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Diagnostic Based on Estimation Using Linear Programming for Partially Observable Petri Nets with Indistinguishable Events

Abstract : In this paper, we design a diagnostic technique for a partially observed labelled Petri net where the faults of the system are modelled by unobservable transitions. The fault detection and isolation uses an on-line count vector estimation associated with the firing of unobservable transitions exploiting the observation of firing occurrences of some observable transitions. The support of the approach is an algebraic description of the process under the form of a polyhedron developed on a receding horizon. We show that a diagnostic can be made despite that different transitions can share the same label and that the unobservable part of the Petri net can contain circuits.
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https://hal.univ-angers.fr/hal-02527961
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Soumis le : mercredi 1 avril 2020 - 15:30:06
Dernière modification le : vendredi 1 mai 2020 - 12:00:55

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Amira Chouchane, Philippe Declerck, Atef Khedher, Anas Kamoun. Diagnostic Based on Estimation Using Linear Programming for Partially Observable Petri Nets with Indistinguishable Events. 2018, Non spécifié. ⟨10.1080/23302674.2018.1554169⟩. ⟨hal-02527961⟩

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