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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/69608
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dc.contributor.authorSilva, Samuel da-
dc.contributor.authorDias Jr., Milton-
dc.contributor.authorLopes Jr., Vicente-
dc.date.accessioned2014-05-27T11:22:27Z-
dc.date.accessioned2016-10-25T18:23:44Z-
dc.date.available2014-05-27T11:22:27Z-
dc.date.available2016-10-25T18:23:44Z-
dc.date.issued2007-04-01-
dc.identifierhttp://dx.doi.org/10.1590/S1678-58782007000200007-
dc.identifier.citationJournal of the Brazilian Society of Mechanical Sciences and Engineering, v. 29, n. 2, p. 174-184, 2007.-
dc.identifier.issn1678-5878-
dc.identifier.issn1806-3691-
dc.identifier.urihttp://hdl.handle.net/11449/69608-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/69608-
dc.description.abstractStructural health monitoring (SHM) is related to the ability of monitoring the state and deciding the level of damage or deterioration within aerospace, civil and mechanical systems. In this sense, this paper deals with the application of a two-step auto-regressive and auto-regressive with exogenous inputs (AR-ARX) model for linear prediction of damage diagnosis in structural systems. This damage detection algorithm is based on the. monitoring of residual error as damage-sensitive indexes, obtained through vibration response measurements. In complex structures there are. many positions under observation and a large amount of data to be handed, making difficult the visualization of the signals. This paper also investigates data compression by using principal component analysis. In order to establish a threshold value, a fuzzy c-means clustering is taken to quantify the damage-sensitive index in an unsupervised learning mode. Tests are made in a benchmark problem, as proposed by IASC-ASCE with different damage patterns. The diagnosis that was obtained showed high correlation with the actual integrity state of the structure. Copyright © 2007 by ABCM.en
dc.format.extent174-184-
dc.language.isoeng-
dc.sourceScopus-
dc.subjectDamage detection-
dc.subjectFuzzy c-means clustering-
dc.subjectPrincipal component analysis-
dc.subjectStructural health monitoring-
dc.subjectTime series-
dc.subjectAerospace applications-
dc.subjectAlgorithms-
dc.subjectData compression-
dc.subjectFuzzy clustering-
dc.subjectMathematical models-
dc.subjectPattern recognition-
dc.subjectTime series analysis-
dc.subjectVibration analysis-
dc.subjectAR-ARX models-
dc.subjectDamage sensitive index-
dc.subjectLinear prediction-
dc.titleDamage detection in a benchmark structure using AR-ARX models and statistical pattern recognitionen
dc.typeoutro-
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.description.affiliationABCM-
dc.description.affiliationDepartment of Mechanical Design Faculty of Mechanical Engineering State University of Campinas - UNICAMP, 13083-970 Campinas, SP-
dc.description.affiliationDepartmentof Mechanical Engineering Universidade Estadual Paulista-UNESP, 15385-000 Ilha Solteira, SP-
dc.description.affiliationUnespDepartmentof Mechanical Engineering Universidade Estadual Paulista-UNESP, 15385-000 Ilha Solteira, SP-
dc.identifier.doi10.1590/S1678-58782007000200007-
dc.identifier.scieloS1678-58782007000200007-
dc.identifier.wosWOS:000255403500007-
dc.rights.accessRightsAcesso aberto-
dc.identifier.file2-s2.0-34548783418.pdf-
dc.relation.ispartofJournal of the Brazilian Society of Mechanical Sciences and Engineering-
dc.identifier.scopus2-s2.0-34548783418-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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