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dc.contributor.authorAgis Cherta, David
dc.contributor.authorTibaduiza Burgos, Diego Alexander
dc.contributor.authorPozo Montero, Francesc
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Matemàtica Aplicada
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Matemàtiques
dc.date.accessioned2020-04-15T10:19:43Z
dc.date.available2020-04-15T10:19:43Z
dc.date.issued2020-03-05
dc.identifier.citationAgis, D.; Tibaduiza, D. A.; Pozo, F. Vibration-based detection and classification of structural changes using principal component analysis and t-distributed stochastic neighbor embedding. "Structural control and health monitoring", 5 Març 2020, vol. 27, núm. 6, p. 2533:1-2533:19.
dc.identifier.issn1545-2263
dc.identifier.urihttp://hdl.handle.net/2117/183436
dc.description.abstractThis paper describes a structural health monitoring strategy to detect and classify structural changes in structures that can be equipped with sensors. The proposed approach is based on the t-distributed stochastic neighbor embedding (t-SNE), a nonlinear technique that can represent the local structure of high-dimensional data collected from multiple sensors in a plane or spatial representation. We propose the following basic steps for the detection and classification. First, the raw data are preprocessed: We scale the data using the mean-centered group scaling and apply principal component analysis to reduce the dimensionality of the scaled data. Second, t-SNE is applied to represent the scaled and reduced data as points in a plane, defining a cluster for each structural state. Finally, the current structure to be diagnosed is associated with a cluster (or structural state) using three different strategies: (a) the smallest point-centroid distance; (b) the majority voting; and (c) the sum of the inverse distances. The combination of t-SNE with our preprocessing and the three proposed classification strategies signif- icantly improves the quality of the clusters that represent different structural states. We evaluate the performance of our method using experimental data from an aluminum plate instrumented with piezoelectric transducers. Results are presented in the time domain, and they reveal the high classification accuracy and strong performance of this method, with a percentage of correct decisions close to 100% in several scenarios.
dc.format.extent19 p.
dc.language.isoeng
dc.publisherWiley
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria civil::Materials i estructures::Càlcul d'estructures
dc.subject.lcshStructural design
dc.subject.otherClassification
dc.subject.otherDetection
dc.subject.otherPrincipal component analysis (PCA)
dc.subject.otherStructural changes
dc.subject.otherStructural health monitoring (SHM)
dc.subject.othert-distributed stochastic neighbor embedding (t-SNE)
dc.titleVibration-based detection and classification of structural changes using principal component analysis and t-distributed stochastic neighbor embedding
dc.typeArticle
dc.subject.lemacDisseny d'estructures
dc.contributor.groupUniversitat Politècnica de Catalunya. CoDAlab - Control, Modelització, Identificació i Aplicacions
dc.identifier.doi10.1002/stc.2533
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://onlinelibrary.wiley.com/journal/15452263
dc.rights.accessOpen Access
local.identifier.drac27749304
dc.description.versionPostprint (author's final draft)
local.citation.authorAgis, D.; Tibaduiza, Diego Alexander; Pozo, F.
local.citation.publicationNameStructural control and health monitoring
local.citation.volume2533:27
local.citation.number6
local.citation.startingPage2533:1
local.citation.endingPage19


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