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dc.contributor.authorFernández Canti, Rosa M.
dc.contributor.authorBlesa Izquierdo, Joaquim
dc.contributor.authorPuig Cayuela, Vicenç
dc.contributor.authorTornil Sin, Sebastián
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.contributor.otherInstitut de Robòtica i Informàtica Industrial
dc.date.accessioned2016-03-18T14:10:52Z
dc.date.available2016-03-18T14:10:52Z
dc.date.issued2016-05-18
dc.identifier.citationFernández-Cantí, R. M., Blesa, J., Puig, V., Tornil-Sin, S. Set-membership identification and fault detection using a Bayesian framework. "International journal of systems science", 18 Maig 2016, vol. 47, núm. 7, p. 1710-1724.
dc.identifier.issn0020-7721
dc.identifier.urihttp://hdl.handle.net/2117/84709
dc.description.abstractThis paper deals with the problem of set-membership identification and fault detection using a Bayesian framework. The paper presents how the set-membership model estimation problem can be reformulated from the Bayesian viewpoint in order to, first, determine the feasible parameter set in the identification stage and, second, check the consistency between the measurement data and the model in the fault-detection stage. The paper shows that, assuming uniform distributed measurement noise and uniform model prior probability distributions, the Bayesian approach leads to the same feasible parameter set than the well-known set-membership technique based on approximating the feasible parameter set using sets. Additionally, it can deal with models that are nonlinear in the parameters. The single-output and multiple-output cases are addressed as well. The procedure and results are illustrated by means of the application to a quadruple-tank process.
dc.format.extent15 p.
dc.language.isoeng
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.subject.lcshControl theory
dc.subject.lcshSystem failures (Engineering)
dc.subject.otherBayes rule
dc.subject.otherFault detection
dc.subject.otherLikelihood function
dc.subject.otherSet-membership identification observability.
dc.titleSet-membership identification and fault detection using a Bayesian framework
dc.typeArticle
dc.subject.lemacControl, Teoria de
dc.subject.lemacAvaries
dc.contributor.groupUniversitat Politècnica de Catalunya. SIC - Sistemes Intel·ligents de Control
dc.contributor.groupUniversitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control
dc.identifier.doi10.1080/00207721.2014.948946
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://www.tandfonline.com/doi/full/10.1080/00207721.2014.948946#.VMDbGtKG8YE
dc.rights.accessOpen Access
local.identifier.drac16638425
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/MICINN/6PN/DPI2009-13744
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/FP7/318556/EU/Efficient Integrated Real-time Monitoring and Control of Drinking Water Networks/EFFINET
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/FP7/270428/EU/Making Sense of Nonsense/ISENSE
local.citation.authorFernández-Cantí, R. M.; Blesa, J.; Puig, V.; Tornil-Sin, S.
local.citation.publicationNameInternational journal of systems science
local.citation.volume47
local.citation.number7
local.citation.startingPage1710
local.citation.endingPage1724


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