Functional diagnosability and detectability of nonlinear models based on analytical redundancy relations
Identifieur interne : 000141 ( Hal/Curation ); précédent : 000140; suivant : 000142Functional diagnosability and detectability of nonlinear models based on analytical redundancy relations
Auteurs : Nathalie Verdière [France] ; Carine Jauberthie [France] ; Louise Travé-Massuyès [France]Source :
- Journal of Process Control [ 0959-1524 ] ; 2015-11.
English descriptors
Abstract
This paper introduces an original definition of diagnosability for nonlinear dynamical models called functional di-agnosability. Fault diagnosability characterizes the faults that can be discriminated using the available sensors in a system. The functional diagnosability definition proposed in this paper is based on analytical redundancy relations obtained from differential algebra tools. Contrary to classical definitions, the study of functional diagnosability highlights some of the analytical redundancy relations properties related to the fault acting on the system. Additionally, it gives a criterion for detecting the faults. Interestingly, the proposed diagnosability definition is closely linked to the notion of identifiability, which establishes an unambiguous mapping between the parameters and the output trajecto-ries of a model. This link allows us to provide a sufficient condition for testing functional diagnosability of a system. Numerical simulations attest the relevance of the suggested approach.
Url:
DOI: 10.1016/j.jprocont.2015.08.001
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<front><div type="abstract" xml:lang="en">This paper introduces an original definition of diagnosability for nonlinear dynamical models called functional di-agnosability. Fault diagnosability characterizes the faults that can be discriminated using the available sensors in a system. The functional diagnosability definition proposed in this paper is based on analytical redundancy relations obtained from differential algebra tools. Contrary to classical definitions, the study of functional diagnosability highlights some of the analytical redundancy relations properties related to the fault acting on the system. Additionally, it gives a criterion for detecting the faults. Interestingly, the proposed diagnosability definition is closely linked to the notion of identifiability, which establishes an unambiguous mapping between the parameters and the output trajecto-ries of a model. This link allows us to provide a sufficient condition for testing functional diagnosability of a system. Numerical simulations attest the relevance of the suggested approach.</div>
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<date type="whenProduced">2015-11</date>
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<respStmt> <resp>contributor</resp>
<name key="195319"> <persName> <forename>Nathalie</forename>
<surname>Verdière</surname>
</persName>
<email>nverdiere@nordnet.fr</email>
</name>
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<idno type="halRefHtml">Journal of Process Control, Elsevier, 2015, 35, pp.1-10. <10.1016/j.jprocont.2015.08.001></idno>
<idno type="halRef">Journal of Process Control, Elsevier, 2015, 35, pp.1-10. <10.1016/j.jprocont.2015.08.001></idno>
</publicationStmt>
<seriesStmt> <idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno>
<idno type="stamp" n="UNIV-LEHAVRE">Université du Havre</idno>
<idno type="stamp" n="INSMI">CNRS-INSMI - INstitut des Sciences Mathématiques et de leurs Interactions</idno>
<idno type="stamp" n="COMUE-NORMANDIE">Normandie Université</idno>
<idno type="stamp" n="LAAS-DECISION-ET-OPTIMISATION" p="LAAS">LAAS-Décision et Optimisation</idno>
<idno type="stamp" n="LAAS-DISCO" p="LAAS-DECISION-ET-OPTIMISATION">Equipe diagnostic, supervision et COnduite</idno>
<idno type="stamp" n="LAAS">Laboratoire d'analyse et d'architecture des systèmes</idno>
<idno type="stamp" n="UNIV-TLSE3">Université Paul Sabatier - Toulouse III</idno>
<idno type="stamp" n="TDS-MACS">Réseau de recherche en Théorie des Systèmes Distribués, Modélisation, Analyse et Contrôle des Systèmes</idno>
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<sourceDesc> <biblStruct> <analytic> <title xml:lang="en">Functional diagnosability and detectability of nonlinear models based on analytical redundancy relations</title>
<author role="aut"> <persName> <forename type="first">Nathalie</forename>
<surname>Verdière</surname>
</persName>
<idno type="halAuthorId">722732</idno>
<affiliation ref="#struct-87"></affiliation>
</author>
<author role="aut"> <persName> <forename type="first">Carine</forename>
<surname>Jauberthie</surname>
</persName>
<idno type="halAuthorId">1150517</idno>
<affiliation ref="#struct-389059"></affiliation>
</author>
<author role="aut"> <persName> <forename type="first">Louise</forename>
<surname>Travé-Massuyès</surname>
</persName>
<idno type="halAuthorId">1176140</idno>
<affiliation ref="#struct-389059"></affiliation>
</author>
</analytic>
<monogr> <idno type="localRef">Rapport LAAS n° 14159</idno>
<idno type="halJournalId" status="VALID">16078</idno>
<idno type="issn">0959-1524</idno>
<title level="j">Journal of Process Control</title>
<imprint> <publisher>Elsevier</publisher>
<biblScope unit="volume">35</biblScope>
<biblScope unit="pp">1-10</biblScope>
<date type="datePub">2015-11</date>
</imprint>
</monogr>
<idno type="doi">10.1016/j.jprocont.2015.08.001</idno>
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</sourceDesc>
<profileDesc> <langUsage> <language ident="en">English</language>
</langUsage>
<textClass> <keywords scheme="author"> <term xml:lang="en">Nonlinear models</term>
<term xml:lang="en">Analytical redundancy relations</term>
<term xml:lang="en">Diagnosability</term>
<term xml:lang="en">Identifiability</term>
</keywords>
<classCode scheme="halDomain" n="math.math-ds">Mathematics [math]/Dynamical Systems [math.DS]</classCode>
<classCode scheme="halTypology" n="ART">Journal articles</classCode>
</textClass>
<abstract xml:lang="en">This paper introduces an original definition of diagnosability for nonlinear dynamical models called functional di-agnosability. Fault diagnosability characterizes the faults that can be discriminated using the available sensors in a system. The functional diagnosability definition proposed in this paper is based on analytical redundancy relations obtained from differential algebra tools. Contrary to classical definitions, the study of functional diagnosability highlights some of the analytical redundancy relations properties related to the fault acting on the system. Additionally, it gives a criterion for detecting the faults. Interestingly, the proposed diagnosability definition is closely linked to the notion of identifiability, which establishes an unambiguous mapping between the parameters and the output trajecto-ries of a model. This link allows us to provide a sufficient condition for testing functional diagnosability of a system. Numerical simulations attest the relevance of the suggested approach.</abstract>
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