A causal mixture model decomposition for ...
Document type :
Communication dans un congrès avec actes
Title :
A causal mixture model decomposition for root cause identification
Author(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]
Conference title :
17th IFAC Symposium on Information Control Problems in Manufacturing
City :
Online
Country :
Hongrie
Start date of the conference :
2021-06-07
HAL domain(s) :
Sciences de l'ingénieur [physics]
English abstract : [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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
Source :