Detection of structural changes through principal component analysis (PCA) and multivariate statistical inference

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/76979

dc.contributor	Universitat Politècnica de Catalunya. Institut de Ciències de l'Educació
dc.contributor	Universitat Politècnica de Catalunya. Departament de Matemàtica Aplicada III
dc.contributor	Universitat Politècnica de Catalunya. CoDAlab - Control, Modelització, Identificació i Aplicacions
dc.contributor.author	Pozo Montero, Francesc
dc.contributor.author	Arruga Cantalapiedra, Ignacio
dc.contributor.author	Mujica Delgado, Luis Eduardo
dc.contributor.author	Podivilova, Elena
dc.date	2015
dc.identifier.citation	Pozo, F., Arruga, I., Mujica, L.E., Podivilova, E. Detection of structural changes through principal component analysis (PCA) and multivariate statistical inference. A: ECCOMAS Thematic Conference Smart Structures and Materials. "SMART 2015 - 7th ECCOMAS Thematic Conference on Smart Structures and Materials: proceedings book". Ponta Delgada, San Miguel, Azores: 2015.
dc.identifier.uri	http://hdl.handle.net/2117/76979
dc.language.iso	eng
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject	Àrees temàtiques de la UPC::Enginyeria civil::Materials i estructures
dc.subject	Structural engineering
dc.subject	damage detection
dc.subject	PCA
dc.subject	multivariate hypothesis testing
dc.subject	structural health monitoring
dc.subject	Enginyeria d'estructures
dc.title	Detection of structural changes through principal component analysis (PCA) and multivariate statistical inference
dc.type	info:eu-repo/semantics/submittedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.abstract	This paper is focused on the development of a damage detection indicator that combines a data driven baseline model (reference pattern obtained from the healthy structure) based on principal component analysis (PCA) and multivariate hypothesis testing. More pre- cisely, a test for the plausibility of a value for a normal population mean vector is performed. The results indicate that the test is able to accurately clasify random samples as healthy or not.
dc.description.abstract	Peer Reviewed

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