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dc.contributor.authorVitola Oyaga, Jaime
dc.contributor.authorPozo Montero, Francesc
dc.contributor.authorTibaduiza Burgos, Diego Alexander
dc.contributor.authorAnaya Vejar, Maribel
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Matemàtiques
dc.date.accessioned2017-03-01T11:26:49Z
dc.date.available2017-03-01T11:26:49Z
dc.date.issued2017-02-21
dc.identifier.citationVitola, J., Pozo, F., Tibaduiza, D.A., Anaya, M. A sensor data fusion system based on k-nearest neighbor pattern classification for structural health monitoring applications. "Sensors", 21 Febrer 2017, vol. 2017, núm. 17, p. 1-26.
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/2117/101779
dc.description.abstractCivil and military structures are susceptible and vulnerable to damage due to the environmental and operational conditions. Therefore, the implementation of technology to provide robust solutions in damage identification (by using signals acquired directly from the structure) is a requirement to reduce operational and maintenance costs. In this sense, the use of sensors permanently attached to the structures has demonstrated a great versatility and benefit since the inspection system can be automated. This automation is carried out with signal processing tasks with the aim of a pattern recognition analysis. This work presents the detailed description of a structural health monitoring (SHM) system based on the use of a piezoelectric (PZT) active system. The SHM system includes: (i) the use of a piezoelectric sensor network to excite the structure and collect the measured dynamic response, in several actuation phases; (ii) data organization; (iii) advanced signal processing techniques to define the feature vectors; and finally; (iv) the nearest neighbor algorithm as a machine learning approach to classify different kinds of damage. A description of the experimental setup, the experimental validation and a discussion of the results from two different structures are included and analyzed.
dc.format.extent26 p.
dc.language.isoeng
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística
dc.subject.lcshDetectors
dc.subject.lcshPattern perception
dc.subject.otherPiezoelectric
dc.subject.othersensors
dc.subject.otheractive system
dc.subject.otherdata fusion
dc.subject.othermachine learning
dc.subject.otherdamage classification
dc.titleA sensor data fusion system based on k-nearest neighbor pattern classification for structural health monitoring applications
dc.typeArticle
dc.subject.lemacSensors
dc.subject.lemacReconeixement de formes (Informàtica)
dc.subject.lemacImatges -- Processament
dc.contributor.groupUniversitat Politècnica de Catalunya. CoDAlab - Control, Modelització, Identificació i Aplicacions
dc.identifier.doi10.3390/s17020417
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://www.mdpi.com/1424-8220/17/2/417
dc.rights.accessOpen Access
local.identifier.drac19724232
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/DPI2014-58427-C2-1-R
local.citation.authorVitola, J.; Pozo, F.; Tibaduiza, D.A.; Anaya, M.
local.citation.publicationNameSensors
local.citation.volume2017
local.citation.number17
local.citation.startingPage1
local.citation.endingPage26


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