Functional diagnosability and detectability of nonlinear models based on analytical redundancy relations
Identifieur interne : 000100 ( Main/Exploration ); précédent : 000099; suivant : 000101Functional 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
Affiliations:
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Toulouse 3</orgName> <orgName type="acronym">UPS</orgName> <desc> <address> <addrLine>118 route de Narbonne - 31062 Toulouse</addrLine> <country key="FR"></country> </address> <ref type="url">http://www.univ-tlse3.fr/</ref> </desc></org></tutelle><tutelle name="UPR8001" active="#struct-441569" type="indirect"><org type="institution" xml:id="struct-441569" status="VALID"><idno type="ISNI">0000000122597504</idno><idno type="IdRef">02636817X</idno><orgName>Centre National de la Recherche Scientifique</orgName><orgName type="acronym">CNRS</orgName><date type="start">1939-10-19</date><desc><address><country key="FR"></country></address><ref type="url">http://www.cnrs.fr/</ref></desc></org></tutelle></tutelles></hal:affiliation><country>France</country></affiliation></author></analytic><idno type="DOI">10.1016/j.jprocont.2015.08.001</idno><series><title level="j">Journal of Process Control</title><idno type="ISSN">0959-1524</idno><imprint><date type="datePub">2015-11</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Analytical redundancy relations</term><term>Diagnosability</term><term>Identifiability</term><term>Nonlinear models</term></keywords></textClass></profileDesc></teiHeader><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></front></TEI><affiliations><list><country><li>France</li></country><region><li>Haute-Normandie</li><li>Région Normandie</li></region><settlement><li>Le Havre</li></settlement><orgName><li>Université du Havre</li></orgName></list><tree><country name="France"><region name="Région Normandie"><name sortKey="Verdiere, Nathalie" sort="Verdiere, Nathalie" uniqKey="Verdiere N" first="Nathalie" last="Verdière">Nathalie Verdière</name></region><name sortKey="Jauberthie, Carine" sort="Jauberthie, Carine" uniqKey="Jauberthie C" first="Carine" last="Jauberthie">Carine Jauberthie</name><name sortKey="Trave Massuyes, Louise" sort="Trave Massuyes, Louise" uniqKey="Trave Massuyes L" first="Louise" last="Travé-Massuyès">Louise Travé-Massuyès</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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