Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition
Autor(a) principal: | |
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Data de Publicação: | 2007 |
Outros Autores: | , |
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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Journal of the Brazilian Society of Mechanical Sciences and Engineering (Online) |
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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 |
_version_ |
1754734680947556352 |