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

Detalhes bibliográficos
Autor(a) principal: Silva,Samuel da
Data de Publicação: 2007
Outros Autores: Dias Júnior,Milton, Lopes Junior,Vicente
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Journal of the Brazilian Society of Mechanical Sciences and Engineering (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1678-58782007000200007
Resumo: Structural 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.
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spelling Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognitionstructural health monitoringdamage detectionprincipal component analysistime seriesfuzzy c-means clusterinStructural 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.Associação Brasileira de Engenharia e Ciências Mecânicas - ABCM2007-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1678-58782007000200007Journal of the Brazilian Society of Mechanical Sciences and Engineering v.29 n.2 2007reponame:Journal of the Brazilian Society of Mechanical Sciences and Engineering (Online)instname:Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM)instacron:ABCM10.1590/S1678-58782007000200007info:eu-repo/semantics/openAccessSilva,Samuel daDias Júnior,MiltonLopes Junior,Vicenteeng2007-09-03T00:00:00Zoai:scielo:S1678-58782007000200007Revistahttps://www.scielo.br/j/jbsmse/https://old.scielo.br/oai/scielo-oai.php||abcm@abcm.org.br1806-36911678-5878opendoar:2007-09-03T00:00Journal of the Brazilian Society of Mechanical Sciences and Engineering (Online) - Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM)false
dc.title.none.fl_str_mv Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition
title Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition
spellingShingle Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition
Silva,Samuel da
structural health monitoring
damage detection
principal component analysis
time series
fuzzy c-means clusterin
title_short Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition
title_full Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition
title_fullStr Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition
title_full_unstemmed Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition
title_sort Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition
author Silva,Samuel da
author_facet Silva,Samuel da
Dias Júnior,Milton
Lopes Junior,Vicente
author_role author
author2 Dias Júnior,Milton
Lopes Junior,Vicente
author2_role author
author
dc.contributor.author.fl_str_mv Silva,Samuel da
Dias Júnior,Milton
Lopes Junior,Vicente
dc.subject.por.fl_str_mv structural health monitoring
damage detection
principal component analysis
time series
fuzzy c-means clusterin
topic structural health monitoring
damage detection
principal component analysis
time series
fuzzy c-means clusterin
description Structural 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.
publishDate 2007
dc.date.none.fl_str_mv 2007-06-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1678-58782007000200007
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1678-58782007000200007
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/S1678-58782007000200007
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Associação Brasileira de Engenharia e Ciências Mecânicas - ABCM
publisher.none.fl_str_mv Associação Brasileira de Engenharia e Ciências Mecânicas - ABCM
dc.source.none.fl_str_mv Journal of the Brazilian Society of Mechanical Sciences and Engineering v.29 n.2 2007
reponame:Journal of the Brazilian Society of Mechanical Sciences and Engineering (Online)
instname:Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM)
instacron:ABCM
instname_str Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM)
instacron_str ABCM
institution ABCM
reponame_str Journal of the Brazilian Society of Mechanical Sciences and Engineering (Online)
collection Journal of the Brazilian Society of Mechanical Sciences and Engineering (Online)
repository.name.fl_str_mv Journal of the Brazilian Society of Mechanical Sciences and Engineering (Online) - Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM)
repository.mail.fl_str_mv ||abcm@abcm.org.br
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