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dc.contributorPozo Montero, Francesc
dc.contributorRodellar Benedé, José
dc.contributor.authorOllé Navarro, Ricard
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Matemàtica Aplicada III
dc.date.accessioned2017-07-06T11:01:49Z
dc.date.available2017-07-06T11:01:49Z
dc.date.issued2016-06-08
dc.identifier.urihttp://hdl.handle.net/2117/106200
dc.description.abstractThis project aims to demonstrate the effectiveness of two fault detection strategies from a wind turbine’s structure, based on obtaining a baseline pattern through the principal component analysis (PCA) on the healthy state of the device’s structure. The data obtained from the structure which we want to check its integrity is projected to the pattern so that we can establish two different hypothesis tests –univariate and multivariate statistical inference- to define whether the structure is damaged or not. It also aims to use these strategies to detect what type of fault affects the wind turbine. To verify the correct operation of the fault detection plans, we will analyse data from structures affected by different types of faults that have been generated from a simulator (FAST software). Thus, we can tell if we are able to distinguish data from a healthy wind turbine or from a faulty one.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.subjectÀrees temàtiques de la UPC::Enginyeria mecànica
dc.subject.lcshWind power
dc.titleFault detection and isolation in wind turbines using PCA and statistical hypothesis testing
dc.typeBachelor thesis
dc.subject.lemacAerogeneradors
dc.rights.accessOpen Access
dc.audience.educationlevelGrau
dc.audience.mediatorEscola d'Enginyeria de Barcelona Est
dc.audience.degreeGRAU EN ENGINYERIA MECÀNICA (Pla 2009)


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