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Diagnosis on a sliding window for partially observable Petri nets

Abstract : In this paper, we propose an algebraic approach to investigate the diagnosis of partially observable labeled Petri nets based on state estimation on a sliding window of a predefined length h. Given an observation, the resulting diagnosis state can be computed while solving integer linear programming problems with a reduced subset of basis markings. The proposed approach consists in exploiting a subset of h observations at each estimation step, which provides a partial diagnosis relevant to the current observation window. This technique allows a status update with a "forgetfulness" of past observations and enables distinguishing repetitive and punctual faults. The complete diagnosis state can be defined as a function of the partial diagnosis states interpreted on the sliding window. As the analysis shows that some basis markings can present an inconsistency with a future evolution, which possibly implies unnecessary computations of basis markings, a withdrawal procedure of these irrelevant basis markings based on linear programming is proposed.
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https://hal.univ-angers.fr/hal-03788361
Contributeur : Philippe Declerck Connectez-vous pour contacter le contributeur
Soumis le : lundi 26 septembre 2022 - 14:24:51
Dernière modification le : lundi 14 novembre 2022 - 02:46:05

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Amira Chouchane, Philippe Declerck. Diagnosis on a sliding window for partially observable Petri nets. Kybernetika, 2022, 58 (4), pp.4 7 9 -4 9 7. ⟨10.14736/kyb-2022-4-0479⟩. ⟨hal-03788361⟩

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