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A single Bayesian network classifier for monitoring with unknown classes

Mohamed Amine Atoui Achraf Cohen Sylvain Verron 1 Abdessamad Kobi 1
1 LARIS - Laboratoire Angevin de Recherche en Ingénierie des Systèmes
Abstract : In this paper, the Conditional Gaussian Networks (CGNs), a form of Bayesian Networks (BN), are used as a statistical process monitoring approach to detect and diagnose faults. The proposed approach improves the structure of Bayesian networks and generalizes a few results regarding statistical tests and the use of an exclusion criterion. The proposed framework is evaluated using data from the benchmark Tennessee Eastman Process (TEP) with various scenarios.
Keywords : Bayesian networks Fault detection and diagnosis Classification Exclusion criteria
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https://hal.univ-angers.fr/hal-02489918
Contributeur : Sylvain Verron <>
Soumis le : lundi 24 février 2020 - 17:28:28
Dernière modification le : dimanche 21 mars 2021 - 13:02:02

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Mohamed Amine Atoui, Achraf Cohen, Sylvain Verron, Abdessamad Kobi. A single Bayesian network classifier for monitoring with unknown classes. Engineering Applications of Artificial Intelligence, Elsevier, 2019, 85, pp.681-690. ⟨10.1016/j.engappai.2019.07.016⟩. ⟨hal-02489918⟩

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