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dc.contributor.authorMoreno Beltran, Gustavo Adolfo
dc.contributor.authorVillamizar Mejía, Rodolfo
dc.contributor.authorCamacho-Navarro, Jhonatan
dc.contributor.authorRuiz Ordóñez, Magda
dc.contributor.authorMujica Delgado, Luis Eduardo
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Matemàtiques
dc.identifier.citationMoreno, G., Villamizar, R., Camacho-Navarro, J., Ruiz, M., Mujica, L.E. Structural damage localization through an innovative hybrid ensemble approach. A: ECCOMAS Thematic Conference Smart Structures and Materials. "SMART 2017: ECCOMAS Thematic Conference on Smart Structures and Materials: Madrid, Espanya: June 5-8, 2017: proceedings book". Madrid: Centre Internacional de Mètodes Numèrics en Enginyeria (CIMNE), 2017, p. 1878-1889.
dc.description.abstractDamage localization in structures can be achieved by using an appropriate data interpretation algorithm based on the expected structural response. According to the several algorithms reported in literature, a different degree of accuracy is obtained according to complexity requirements. This paper presents a hybrid algorithm approach as alternative to combine some of the reported methods by employing an ensemble architecture. Thus, this damage assessment algorithm integrates advantage of individual techniques in order to increase the performance of the whole expert system. The proposed architecture employs a network of piezoelectric devices to produce guided waves along the structure. The traveling of guided waves is affected by damage producing scattering, reflection and mode conversion, which can be characterized with statistical processing and pattern recognition methods. In this paper, supervised learning by means on ensemble learning, cross-correlation features, and PCA statistical indices are combined for locating damages. An experimental validation is conducted on an aircraft turbine blade structure instrumented with an array of piezoelectric devices (PZT), where it is demonstrated the potential of the methodology to significantly enhance localization tasks.
dc.format.extent12 p.
dc.publisherCentre Internacional de Mètodes Numèrics en Enginyeria (CIMNE)
dc.subjectÀrees temàtiques de la UPC::Enginyeria elèctrica
dc.subject.lcshPrincipal components analysis
dc.subject.lcshSupervised study
dc.subject.otherDamage localization
dc.subject.otherEnsemble learning
dc.subject.otherPrincipal Component Analysis
dc.subject.otherSupervised methods
dc.titleStructural damage localization through an innovative hybrid ensemble approach
dc.typeConference report
dc.subject.lemacAnàlisi de components principals
dc.contributor.groupUniversitat Politècnica de Catalunya. CoDAlab - Control, Modelització, Identificació i Aplicacions
dc.rights.accessRestricted access - publisher's policy
dc.description.versionPostprint (published version)
local.citation.authorMoreno, G.; Villamizar, R.; Camacho-Navarro, J.; Ruiz, M.; Mujica, L.E.
local.citation.contributorECCOMAS Thematic Conference Smart Structures and Materials
local.citation.publicationNameSMART 2017: ECCOMAS Thematic Conference on Smart Structures and Materials: Madrid, Espanya: June 5-8, 2017: proceedings book

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