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dc.contributorPuig Cayuela, Vicenç
dc.contributorBlesa Izquierdo, Joaquim
dc.contributor.authorFernández Canti, Rosa Ma.
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2013-06-11T12:02:09Z
dc.date.available2013-06-11T12:02:09Z
dc.date.issued2013-02-07
dc.identifier.citationFernández Canti, R. M. "A Bayesian approach to robust identification: application to fault detection". Tesi doctoral, UPC, Departament de Teoria del Senyal i Comunicacions, 2013.
dc.identifier.urihttp://hdl.handle.net/2117/94848
dc.description.abstractIn the Control Engineering field, the so-called Robust Identification techniques deal with the problem of obtaining not only a nominal model of the plant, but also an estimate of the uncertainty associated to the nominal model. Such model of uncertainty is typically characterized as a region in the parameter space or as an uncertainty band around the frequency response of the nominal model. Uncertainty models have been widely used in the design of robust controllers and, recently, their use in model-based fault detection procedures is increasing. In this later case, consistency between new measurements and the uncertainty region is checked. When an inconsistency is found, the existence of a fault is decided. There exist two main approaches to the modeling of model uncertainty: the deterministic/worst case methods and the stochastic/probabilistic methods. At present, there are a number of different methods, e.g., model error modeling, set-membership identification and non-stationary stochastic embedding. In this dissertation we summarize the main procedures and illustrate their results by means of several examples of the literature. As contribution we propose a Bayesian methodology to solve the robust identification problem. The approach is highly unifying since many robust identification techniques can be interpreted as particular cases of the Bayesian framework. Also, the methodology can deal with non-linear structures such as the ones derived from the use of observers. The obtained Bayesian uncertainty models are used to detect faults in a quadruple-tank process and in a three-bladed wind turbine.
dc.format.extent216 p.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.rightsADVERTIMENT. L'accés als continguts d'aquesta tesi doctoral i la seva utilització ha de respectar els drets de la persona autora. Pot ser utilitzada per a consulta o estudi personal, així com en activitats o materials d'investigació i docència en els termes establerts a l'art. 32 del Text Refós de la Llei de Propietat Intel·lectual (RDL 1/1996). Per altres utilitzacions es requereix l'autorització prèvia i expressa de la persona autora. En qualsevol cas, en la utilització dels seus continguts caldrà indicar de forma clara el nom i cognoms de la persona autora i el títol de la tesi doctoral. No s'autoritza la seva reproducció o altres formes d'explotació efectuades amb finalitats de lucre ni la seva comunicació pública des d'un lloc aliè al servei TDX. Tampoc s'autoritza la presentació del seu contingut en una finestra o marc aliè a TDX (framing). Aquesta reserva de drets afecta tant als continguts de la tesi com als seus resums i índexs.
dc.sourceTDX (Tesis Doctorals en Xarxa)
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació
dc.titleA Bayesian approach to robust identification: application to fault detection
dc.typeDoctoral thesis
dc.identifier.dlB. 16897-2013
dc.rights.accessOpen Access
dc.description.versionPostprint (published version)
dc.identifier.tdxhttp://hdl.handle.net/10803/116329


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