Unlocked decision making based on causal ...
Type de document :
Communication dans un congrès avec actes
Titre :
Unlocked decision making based on causal connections strength
Auteur(s) :
Atoui, Mohamed Amine [Auteur]
Télécommunication, Interférences et Compatibilité Electromagnétique - IEMN [TELICE - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Cohen, Achraf [Auteur]
Cocquempot, Vincent [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Télécommunication, Interférences et Compatibilité Electromagnétique - IEMN [TELICE - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Cohen, Achraf [Auteur]
Cocquempot, Vincent [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Titre de la manifestation scientifique :
European Control Conference 2021
Ville :
Online
Pays :
Pays-Bas
Date de début de la manifestation scientifique :
2021-06-29
Discipline(s) HAL :
Sciences de l'ingénieur [physics]
Résumé en anglais : [en]
Fault detection and diagnosis are crucial to reducing risks and costs in any process. The identification of the propagation path and the variables responsible for faulty operating conditions is also vital. This paper ...
Lire la suite >Fault detection and diagnosis are crucial to reducing risks and costs in any process. The identification of the propagation path and the variables responsible for faulty operating conditions is also vital. This paper presents a causal network-based approach to detect, diagnose, and identify root causes in multivariate processes. We discuss aspects such as complexity and rules related to modeling such network approaches. The proposed strategy is established on statistical justifications. The introduced decision rules deal with unknown faults and offer new perspectives to data-driven methods for fault diagnosis. The proposed approach is evaluated and demonstrated using the well-known Tennessee Eastman Process (TEP) benchmark.Lire moins >
Lire la suite >Fault detection and diagnosis are crucial to reducing risks and costs in any process. The identification of the propagation path and the variables responsible for faulty operating conditions is also vital. This paper presents a causal network-based approach to detect, diagnose, and identify root causes in multivariate processes. We discuss aspects such as complexity and rules related to modeling such network approaches. The proposed strategy is established on statistical justifications. The introduced decision rules deal with unknown faults and offer new perspectives to data-driven methods for fault diagnosis. The proposed approach is evaluated and demonstrated using the well-known Tennessee Eastman Process (TEP) benchmark.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :