Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition

dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorSilva, Samuel da
dc.creatorDias Jr., Milton
dc.creatorLopes Jr., Vicente
dc.date2014-05-27T11:22:27Z
dc.date2016-10-25T18:23:44Z
dc.date2014-05-27T11:22:27Z
dc.date2016-10-25T18:23:44Z
dc.date2007-04-01
dc.date.accessioned2017-04-06T01:24:24Z
dc.date.available2017-04-06T01:24:24Z
dc.identifierJournal of the Brazilian Society of Mechanical Sciences and Engineering, v. 29, n. 2, p. 174-184, 2007.
dc.identifier1678-5878
dc.identifier1806-3691
dc.identifierhttp://hdl.handle.net/11449/69608
dc.identifierhttp://acervodigital.unesp.br/handle/11449/69608
dc.identifier10.1590/S1678-58782007000200007
dc.identifierS1678-58782007000200007
dc.identifierWOS:000255403500007
dc.identifier2-s2.0-34548783418.pdf
dc.identifier2-s2.0-34548783418
dc.identifierhttp://dx.doi.org/10.1590/S1678-58782007000200007
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/890827
dc.descriptionStructural 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.
dc.languageeng
dc.relationJournal of the Brazilian Society of Mechanical Sciences and Engineering
dc.rightsinfo:eu-repo/semantics/openAccess
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 recognition
dc.typeOtro


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