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

dc.creatorda Silva, S
dc.creatorDias, M
dc.creatorLopes, V
dc.date2007
dc.dateAPR-JUN
dc.date2014-11-15T17:55:07Z
dc.date2015-11-26T16:12:49Z
dc.date2014-11-15T17:55:07Z
dc.date2015-11-26T16:12:49Z
dc.date.accessioned2018-03-28T23:00:47Z
dc.date.available2018-03-28T23:00:47Z
dc.identifierJournal Of The Brazilian Society Of Mechanical Sciences And Engineering. Abcm Brazilian Soc Mechanical Sciences & Engineering, v. 29, n. 2, n. 174, n. 184, 2007.
dc.identifier1678-5878
dc.identifier1806-3691
dc.identifierWOS:000255403500007
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/79164
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/79164
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/79164
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1267296
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 art 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.
dc.description29
dc.description2
dc.description174
dc.description184
dc.languageen
dc.publisherAbcm Brazilian Soc Mechanical Sciences & Engineering
dc.publisherRio De Janeiro Rj
dc.publisherBrasil
dc.relationJournal Of The Brazilian Society Of Mechanical Sciences And Engineering
dc.relationJ. Braz. Soc. Mech. Sci. Eng.
dc.rightsaberto
dc.sourceWeb of Science
dc.subjectstructural health monitoring
dc.subjectdamage detection
dc.subjectprincipal component analysis
dc.subjecttime series
dc.subjectfuzzy
dc.subjectc-means clustering
dc.subjectOutlier Analysis
dc.subjectIdentification
dc.subjectDiagnosis
dc.titleDamage detection in a benchmark structure using AR-ARX models and statistical pattern recognition
dc.typeArtículos de revistas


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