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dc.contributor.authorFernández Canti, Rosa M.
dc.contributor.authorTornil Sin, Sebastián
dc.contributor.authorBlesa Izquierdo, Joaquim
dc.contributor.authorPuig Cayuela, Vicenç
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
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.accessioned2014-03-20T14:59:15Z
dc.date.created2013
dc.date.issued2013
dc.identifier.citationFernández-Cantí, R. M. [et al.]. Nonlinear set-membership identification and fault detection using a Bayesian framework: appllication to the wind turbine benchmark. A: IEEE Conference on Decision and Control. "Proceedings of the 2013 IEEE 52nd Annual Conference on Decision and Control: CDC 2013: December 10-13, 2013: Florence, Italy". Florència: Institute of Electrical and Electronics Engineers (IEEE), 2013, p. 496-501.
dc.identifier.isbn978-1-4673-5714-2
dc.identifier.urihttp://hdl.handle.net/2117/22319
dc.description.abstractThis paper deals with the problem of nonlinear set-membership identification and fault detection using a Bayesian framework. The paper presents how the set-membership model estimation can be reformulated from a Bayesian viewpoint in order to determine the feasible parameter set and, in a posterior fault detection stage, to check the consistency between the model and the data. The paper shows that the Bayesian approach, assuming uniform distributed measurement noise and flat model prior probability distribution, leads to the same feasible parameter set as the set-membership technique. To illustrate this point a comparison with the subpavings approach is included. Finally, by means of the application to the wind turbine benchmark problem, it is shown how the Bayesian fault detection test works successfully.
dc.format.extent6 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.subjectÀrees temàtiques de la UPC::Energies::Energia eòlica::Aerogeneradors
dc.subject.lcshWind turbines -- Automatic control
dc.subject.otherNonlinear set-membership identification
dc.subject.otherFault detection
dc.subject.otherWind turbine
dc.titleNonlinear set-membership identification and fault detection using a Bayesian framework: appllication to the wind turbine benchmark
dc.typeConference report
dc.subject.lemacEnergia eòlica
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.doi10.1109/CDC.2013.6759930
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6759930
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac12998423
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
local.citation.authorFernández-Cantí, R. M.; Tornil-Sin, S.; Blesa, J.; Puig, V.
local.citation.contributorIEEE Conference on Decision and Control
local.citation.pubplaceFlorència
local.citation.publicationNameProceedings of the 2013 IEEE 52nd Annual Conference on Decision and Control: CDC 2013: December 10-13, 2013: Florence, Italy
local.citation.startingPage496
local.citation.endingPage501


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