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Nonlinear set-membership identification and fault detection using a Bayesian framework: appllication to the wind turbine benchmark
dc.contributor.author | Fernández Canti, Rosa M. |
dc.contributor.author | Tornil Sin, Sebastián |
dc.contributor.author | Blesa Izquierdo, Joaquim |
dc.contributor.author | Puig Cayuela, Vicenç |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial |
dc.contributor.other | Institut de Robòtica i Informàtica Industrial |
dc.date.accessioned | 2014-03-20T14:59:15Z |
dc.date.created | 2013 |
dc.date.issued | 2013 |
dc.identifier.citation | Fernández-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.isbn | 978-1-4673-5714-2 |
dc.identifier.uri | http://hdl.handle.net/2117/22319 |
dc.description.abstract | This 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.extent | 6 p. |
dc.language.iso | eng |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Automàtica i control |
dc.subject | Àrees temàtiques de la UPC::Energies::Energia eòlica::Aerogeneradors |
dc.subject.lcsh | Wind turbines -- Automatic control |
dc.subject.other | Nonlinear set-membership identification |
dc.subject.other | Fault detection |
dc.subject.other | Wind turbine |
dc.title | Nonlinear set-membership identification and fault detection using a Bayesian framework: appllication to the wind turbine benchmark |
dc.type | Conference report |
dc.subject.lemac | Energia eòlica |
dc.contributor.group | Universitat Politècnica de Catalunya. SIC - Sistemes Intel·ligents de Control |
dc.contributor.group | Universitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control |
dc.identifier.doi | 10.1109/CDC.2013.6759930 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6759930 |
dc.rights.access | Restricted access - publisher's policy |
local.identifier.drac | 12998423 |
dc.description.version | Postprint (published version) |
dc.date.lift | 10000-01-01 |
local.citation.author | Fernández-Cantí, R. M.; Tornil-Sin, S.; Blesa, J.; Puig, V. |
local.citation.contributor | IEEE Conference on Decision and Control |
local.citation.pubplace | Florència |
local.citation.publicationName | Proceedings of the 2013 IEEE 52nd Annual Conference on Decision and Control: CDC 2013: December 10-13, 2013: Florence, Italy |
local.citation.startingPage | 496 |
local.citation.endingPage | 501 |