A single Bayesian network classifier for monitoring with unknown classes

Mohamed Amine Atoui; Achraf Cohen; Sylvain Verron; Abdessamad Kobi

doi:10.1016/j.engappai.2019.07.016

Article dans une revue

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

Type de document :

Article dans une revue

Domaine :

Sciences de l'ingénieur [physics] / Automatique / Robotique

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https://hal.univ-angers.fr/hal-02489918
Contributeur : Sylvain Verron Connectez-vous pour contacter le contributeur
Soumis le : lundi 24 février 2020 - 17:28:28
Dernière modification le : vendredi 11 mars 2022 - 16:36:02

A single Bayesian network classifier for monitoring with unknown classes

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A single Bayesian network classifier for monitoring with unknown classes

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