Structural Analysis for FDI: a modified, ...
Type de document :
Communication dans un congrès avec actes
Titre :
Structural Analysis for FDI: a modified, invertibility-based canonical decomposition
Auteur(s) :
de Flaugergues, Vincent [Auteur]
Cocquempot, Vincent [Auteur]
Systèmes Tolérants aux Fautes [STF]
Merchez, Mireille [Auteur]
Pengov, Marco [Auteur]
Cocquempot, Vincent [Auteur]
Systèmes Tolérants aux Fautes [STF]
Merchez, Mireille [Auteur]
Pengov, Marco [Auteur]
Titre de la manifestation scientifique :
20th International Workshop on Principles of Diagnosis DX'09
Ville :
Stockholm
Pays :
Suède
Date de début de la manifestation scientifique :
2009-06-14
Titre de l’ouvrage :
Proceedings of the 20th International Workshop on Principles of Diagnosis
Date de publication :
2009-06-14
Mot(s)-clé(s) en anglais :
Fault detection and diagnosis
structural analysis
model decomposition
invertibility
structural analysis
model decomposition
invertibility
Discipline(s) HAL :
Sciences de l'ingénieur [physics]/Automatique / Robotique
Résumé en anglais : [en]
Structural analysis is a simple but efficient method in the field of Fault Detection and Isolation (FDI), to determine system properties such as observability, fault detectability or diagnosability. Our method is based on ...
Lire la suite >Structural analysis is a simple but efficient method in the field of Fault Detection and Isolation (FDI), to determine system properties such as observability, fault detectability or diagnosability. Our method is based on the study of a bipartite graph derived from the behavioral model of the system, which represents the links between the variables of the system. A graph-decomposition tool, known as the Dulmage-Mendelsohn decomposition (also named canonical decomposition) is used in order to determine the monitorable and observable subsystems. Additionally, a structural analysis can be performed with the objective of designing fault indicators, i.e. residuals, which are used for FDI. Invertibility constraints of the relations of the behavioral model, are used to determine the calculability of the residuals. These invertibility constraints are not considered to derive the canonical decomposition. Consequently, some structurally monitorable subsystems may correspond to non-realizable (non-computable) residuals. In this paper, we propose a new canonical decomposition algorithm which takes into account the invertibility constraints: our modified decomposition redefines the monitorable and observable part of the system, so that each of these parts do not contain elements which would turn out to be unusable, from a calculability standpoint.Lire moins >
Lire la suite >Structural analysis is a simple but efficient method in the field of Fault Detection and Isolation (FDI), to determine system properties such as observability, fault detectability or diagnosability. Our method is based on the study of a bipartite graph derived from the behavioral model of the system, which represents the links between the variables of the system. A graph-decomposition tool, known as the Dulmage-Mendelsohn decomposition (also named canonical decomposition) is used in order to determine the monitorable and observable subsystems. Additionally, a structural analysis can be performed with the objective of designing fault indicators, i.e. residuals, which are used for FDI. Invertibility constraints of the relations of the behavioral model, are used to determine the calculability of the residuals. These invertibility constraints are not considered to derive the canonical decomposition. Consequently, some structurally monitorable subsystems may correspond to non-realizable (non-computable) residuals. In this paper, we propose a new canonical decomposition algorithm which takes into account the invertibility constraints: our modified decomposition redefines the monitorable and observable part of the system, so that each of these parts do not contain elements which would turn out to be unusable, from a calculability standpoint.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :