A causal mixture model decomposition for ...
Type de document :
Communication dans un congrès avec actes
Titre :
A causal mixture model decomposition for root cause identification
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]
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]
Cocquempot, Vincent [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Titre de la manifestation scientifique :
17th IFAC Symposium on Information Control Problems in Manufacturing
Ville :
Online
Pays :
Hongrie
Date de début de la manifestation scientifique :
2021-06-07
Discipline(s) HAL :
Sciences de l'ingénieur [physics]
Résumé en anglais : [en]
Multivariate statistical process monitoring methods usually assume the Gaussianityof data. However, in practice, data are multi-modal. Therefore, it’s not always reasonable andenough to use methods that only deal with the ...
Lire la suite >Multivariate statistical process monitoring methods usually assume the Gaussianityof data. However, in practice, data are multi-modal. Therefore, it’s not always reasonable andenough to use methods that only deal with the data overall covariance matrix. As the lattermay wrap less information compared to the data distribution. Also, such prior assumption is prejudicial to the estimation of the data’ structure and the causal direction of variables. An interesting challenge would then be the development of relevant metrics to monitor variablesand address their causal nature in the context of the non-Gaussianity of the data. Therefore, adequate parametric tests are required to ensure an acceptable and adjustable compromise between false positives and false negatives. In this paper, a new statistical approach is introducedto root cause and fault path propagation analysis. The obtained results demonstrate that theproposed method performs better than the existing methods.Lire moins >
Lire la suite >Multivariate statistical process monitoring methods usually assume the Gaussianityof data. However, in practice, data are multi-modal. Therefore, it’s not always reasonable andenough to use methods that only deal with the data overall covariance matrix. As the lattermay wrap less information compared to the data distribution. Also, such prior assumption is prejudicial to the estimation of the data’ structure and the causal direction of variables. An interesting challenge would then be the development of relevant metrics to monitor variablesand address their causal nature in the context of the non-Gaussianity of the data. Therefore, adequate parametric tests are required to ensure an acceptable and adjustable compromise between false positives and false negatives. In this paper, a new statistical approach is introducedto root cause and fault path propagation analysis. The obtained results demonstrate that theproposed method performs better than the existing methods.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :