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: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1590/S1678-58782007000200007 http://hdl.handle.net/11449/69608 |
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. Copyright © 2007 by ABCM. |
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Repositório Institucional da UNESP |
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spelling |
Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognitionDamage detectionFuzzy c-means clusteringPrincipal component analysisStructural health monitoringTime seriesAerospace applicationsAlgorithmsData compressionFuzzy clusteringMathematical modelsPattern recognitionTime series analysisVibration analysisAR-ARX modelsDamage sensitive indexLinear predictionStructural 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.ABCMDepartment of Mechanical Design Faculty of Mechanical Engineering State University of Campinas - UNICAMP, 13083-970 Campinas, SPDepartmentof Mechanical Engineering Universidade Estadual Paulista-UNESP, 15385-000 Ilha Solteira, SPDepartmentof Mechanical Engineering Universidade Estadual Paulista-UNESP, 15385-000 Ilha Solteira, SPUniversidade Estadual de Campinas (UNICAMP)Universidade Estadual Paulista (Unesp)Silva, Samuel daDias Jr., MiltonLopes Jr., Vicente [UNESP]2014-05-27T11:22:27Z2014-05-27T11:22:27Z2007-04-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article174-184application/pdfhttp://dx.doi.org/10.1590/S1678-58782007000200007Journal of the Brazilian Society of Mechanical Sciences and Engineering, v. 29, n. 2, p. 174-184, 2007.1678-58781806-3691http://hdl.handle.net/11449/6960810.1590/S1678-58782007000200007S1678-58782007000200007WOS:0002554035000072-s2.0-345487834182-s2.0-34548783418.pdf1457178419328525Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of the Brazilian Society of Mechanical Sciences and Engineering1.6270,362info:eu-repo/semantics/openAccess2023-11-03T06:13:15Zoai:repositorio.unesp.br:11449/69608Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-11-03T06:13:15Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)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 Damage detection Fuzzy c-means clustering Principal component analysis Structural health monitoring Time series Aerospace applications Algorithms Data compression Fuzzy clustering Mathematical models Pattern recognition Time series analysis Vibration analysis AR-ARX models Damage sensitive index Linear prediction |
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 Jr., Milton Lopes Jr., Vicente [UNESP] |
author_role |
author |
author2 |
Dias Jr., Milton Lopes Jr., Vicente [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual de Campinas (UNICAMP) Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Silva, Samuel da Dias Jr., Milton Lopes Jr., Vicente [UNESP] |
dc.subject.por.fl_str_mv |
Damage detection Fuzzy c-means clustering Principal component analysis Structural health monitoring Time series Aerospace applications Algorithms Data compression Fuzzy clustering Mathematical models Pattern recognition Time series analysis Vibration analysis AR-ARX models Damage sensitive index Linear prediction |
topic |
Damage detection Fuzzy c-means clustering Principal component analysis Structural health monitoring Time series Aerospace applications Algorithms Data compression Fuzzy clustering Mathematical models Pattern recognition Time series analysis Vibration analysis AR-ARX models Damage sensitive index Linear prediction |
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. Copyright © 2007 by ABCM. |
publishDate |
2007 |
dc.date.none.fl_str_mv |
2007-04-01 2014-05-27T11:22:27Z 2014-05-27T11:22:27Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1590/S1678-58782007000200007 Journal of the Brazilian Society of Mechanical Sciences and Engineering, v. 29, n. 2, p. 174-184, 2007. 1678-5878 1806-3691 http://hdl.handle.net/11449/69608 10.1590/S1678-58782007000200007 S1678-58782007000200007 WOS:000255403500007 2-s2.0-34548783418 2-s2.0-34548783418.pdf 1457178419328525 |
url |
http://dx.doi.org/10.1590/S1678-58782007000200007 http://hdl.handle.net/11449/69608 |
identifier_str_mv |
Journal of the Brazilian Society of Mechanical Sciences and Engineering, v. 29, n. 2, p. 174-184, 2007. 1678-5878 1806-3691 10.1590/S1678-58782007000200007 S1678-58782007000200007 WOS:000255403500007 2-s2.0-34548783418 2-s2.0-34548783418.pdf 1457178419328525 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Journal of the Brazilian Society of Mechanical Sciences and Engineering 1.627 0,362 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
174-184 application/pdf |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1792961728885030912 |