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dc.contributor.authorAgis Cherta, David
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.accessioned2020-03-20T08:11:11Z
dc.date.available2020-03-20T08:11:11Z
dc.date.issued2020-03-19
dc.identifier.citationAgis, D.; Pozo, F. Vibration-Based structural health monitoring using piezoelectric transducers and parametric t-SNE. "Sensors", 19 Març 2020, vol. 20, núm. 6, p. 1716:1-1716:17.
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/2117/180633
dc.description.abstractIn this paper, we evaluate the performance of the so-called parametric t-distributed stochastic neighbor embedding (P-t-SNE), comparing it to the performance of the t-SNE, the non-parametric version. The methodology used in this study is introduced for the detection and classification of structural changes in the field of structural health monitoring. This method is based on the combination of principal component analysis (PCA) and P-t-SNE, and it is applied to an experimental case study of an aluminum plate with four piezoelectric transducers. The basic steps of the detection and classification process are: (i) the raw data are scaled using mean-centered group scaling and then PCA is applied to reduce its dimensionality; (ii) P-t-SNE is applied to represent the scaled and reduced data as 2-dimensional points, defining a cluster for each structural state; and (iii) the current structure to be diagnosed is associated with a cluster employing two strategies: (a) majority voting; and (b) the sum of the inverse distances. The results in the frequency domain manifest the strong performance of P-t-SNE, which is comparable to the performance of t-SNE but outperforms t-SNE in terms of computational cost and runtime. When the method is based on P-t-SNE, the overall accuracy fluctuates between 99.5% and 99.75%.
dc.language.isoeng
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
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 elèctrica
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística
dc.subjectÀrees temàtiques de la UPC::Energies::Energia elèctrica::Automatització i control de l'energia elèctrica
dc.subject.lcshEfecte piezoelèctric
dc.subject.lcshVibration transducers
dc.subject.lcshRandom vibration
dc.subject.otherClassification
dc.subject.otherDetection
dc.subject.otherParametric t-distributed stochastic neighbor embedding (P-t-SNE)
dc.subject.otherPiezoelectric transducers (PZTs)
dc.subject.otherPrincipal component analysis (PCA)
dc.subject.otherStructural health monitoring (SHM)
dc.subject.otherVibration-based SHM
dc.titleVibration-Based structural health monitoring using piezoelectric transducers and parametric t-SNE
dc.typeArticle
dc.subject.lemacPiezoelectricitat
dc.subject.lemacTransductors de vibració
dc.subject.lemacVibració aleatòria
dc.contributor.groupUniversitat Politècnica de Catalunya. CoDAlab - Control, Modelització, Identificació i Aplicacions
dc.identifier.doi10.3390/s20061716
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.mdpi.com/journal/sensors
dc.rights.accessOpen Access
local.identifier.drac27592989
dc.description.versionPostprint (published version)
local.citation.authorAgis, D.; Pozo, F.
local.citation.publicationNameSensors
local.citation.volume20
local.citation.number6
local.citation.startingPage1716:1
local.citation.endingPage1716:17


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Attribution-NonCommercial-NoDerivs 3.0 Spain
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