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A sensor data fusion system based on k-nearest neighbor pattern classification for structural health monitoring applications
dc.contributor.author | Vitola Oyaga, Jaime |
dc.contributor.author | Pozo Montero, Francesc |
dc.contributor.author | Tibaduiza Burgos, Diego Alexander |
dc.contributor.author | Anaya Vejar, Maribel |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Matemàtiques |
dc.date.accessioned | 2017-03-01T11:26:49Z |
dc.date.available | 2017-03-01T11:26:49Z |
dc.date.issued | 2017-02-21 |
dc.identifier.citation | Vitola, 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.issn | 1424-8220 |
dc.identifier.uri | http://hdl.handle.net/2117/101779 |
dc.description.abstract | Civil 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.extent | 26 p. |
dc.language.iso | eng |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Matemàtiques i estadística |
dc.subject.lcsh | Detectors |
dc.subject.lcsh | Pattern perception |
dc.subject.other | Piezoelectric |
dc.subject.other | sensors |
dc.subject.other | active system |
dc.subject.other | data fusion |
dc.subject.other | machine learning |
dc.subject.other | damage classification |
dc.title | A sensor data fusion system based on k-nearest neighbor pattern classification for structural health monitoring applications |
dc.type | Article |
dc.subject.lemac | Sensors |
dc.subject.lemac | Reconeixement de formes (Informàtica) |
dc.subject.lemac | Imatges -- Processament |
dc.contributor.group | Universitat Politècnica de Catalunya. CoDAlab - Control, Modelització, Identificació i Aplicacions |
dc.identifier.doi | 10.3390/s17020417 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://www.mdpi.com/1424-8220/17/2/417 |
dc.rights.access | Open Access |
local.identifier.drac | 19724232 |
dc.description.version | Postprint (published version) |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO//DPI2014-58427-C2-1-R/ES/DESARROLLO Y VALIDACION DE SISTEMAS DE DETECCION DE FALLOS Y DISEÑO DE ESTRATEGIAS DE CONTROL TOLERANTE A FALLOS CON APLICACION A PLANTAS DE ENERGIA EOLICA OFFSHORE/ |
local.citation.author | Vitola, J.; Pozo, F.; Tibaduiza, D.A.; Anaya, M. |
local.citation.publicationName | Sensors |
local.citation.volume | 2017 |
local.citation.number | 17 |
local.citation.startingPage | 1 |
local.citation.endingPage | 26 |
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