Fuzzy clustering and AR models for damage detection in CFRP coupons considering loading effect

Detalhes bibliográficos
Autor(a) principal: Cano, Wagner Francisco Rezende
Data de Publicação: 2020
Outros Autores: da Silva, Samuel [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/s40430-020-02304-7
http://hdl.handle.net/11449/198756
Resumo: This paper proposes a strategy to avoid false alarms by distinguishing operation effects from damages effects in composite laminates. This strategy is based on active and sensing piezoelectric patches receiving Lamb waves that can be profoundly affected by operational factors such as load leading to false diagnostics. In order to overcome this drawback, this paper proposes an approach analyzing the use of prediction errors obtained by auto-regressive (AR) models. This index is computed using only the output signal received from sensors and combined with other traditional sensitive-damage indices. The fuzzy clustering technique is then applied for distinguishing the load effects from the effects of the damage. The method is evaluated using a carbon fiber-reinforced polymer coupons subject to tension–tension fatigue and with layers of piezoelectric sensors and actuators bonded on this surface. The results revealed that fuzzy clustering using a fuzzy c-means (FCM) algorithm could distinguish these effects using one-step-ahead AR errors combined with other standard indices extracted in time and frequency domains. This strategy may be easily implemented for signal processing, making possible its online application in a real-world structure.
id UNSP_8557f7f9c98d793399894501ed87bb81
oai_identifier_str oai:repositorio.unesp.br:11449/198756
network_acronym_str UNSP
network_name_str Repositório Institucional da UNESP
repository_id_str 2946
spelling Fuzzy clustering and AR models for damage detection in CFRP coupons considering loading effectAR modelsComposite materialsDamage detectionFuzzy clusteringLamb wavesLoad variationsSmart StructuresThis paper proposes a strategy to avoid false alarms by distinguishing operation effects from damages effects in composite laminates. This strategy is based on active and sensing piezoelectric patches receiving Lamb waves that can be profoundly affected by operational factors such as load leading to false diagnostics. In order to overcome this drawback, this paper proposes an approach analyzing the use of prediction errors obtained by auto-regressive (AR) models. This index is computed using only the output signal received from sensors and combined with other traditional sensitive-damage indices. The fuzzy clustering technique is then applied for distinguishing the load effects from the effects of the damage. The method is evaluated using a carbon fiber-reinforced polymer coupons subject to tension–tension fatigue and with layers of piezoelectric sensors and actuators bonded on this surface. The results revealed that fuzzy clustering using a fuzzy c-means (FCM) algorithm could distinguish these effects using one-step-ahead AR errors combined with other standard indices extracted in time and frequency domains. This strategy may be easily implemented for signal processing, making possible its online application in a real-world structure.Fundação Centro de Pesquisa e Desenvolvimento em Telecomunicações - CPqD, Rua Dr. Ricardo Benetton Martins, 1000Departamento de Engenharia Mecânica Faculdade de Engenharia UNESP - Universidade Estadual Paulista, Av. Brasil 56Departamento de Engenharia Mecânica Faculdade de Engenharia UNESP - Universidade Estadual Paulista, Av. Brasil 56Fundação Centro de Pesquisa e Desenvolvimento em Telecomunicações - CPqDUniversidade Estadual Paulista (Unesp)Cano, Wagner Francisco Rezendeda Silva, Samuel [UNESP]2020-12-12T01:21:13Z2020-12-12T01:21:13Z2020-05-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1007/s40430-020-02304-7Journal of the Brazilian Society of Mechanical Sciences and Engineering, v. 42, n. 5, 2020.1806-36911678-5878http://hdl.handle.net/11449/19875610.1007/s40430-020-02304-72-s2.0-85083645813Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of the Brazilian Society of Mechanical Sciences and Engineeringinfo:eu-repo/semantics/openAccess2021-10-22T20:11:21Zoai:repositorio.unesp.br:11449/198756Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T18:23:46.586757Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Fuzzy clustering and AR models for damage detection in CFRP coupons considering loading effect
title Fuzzy clustering and AR models for damage detection in CFRP coupons considering loading effect
spellingShingle Fuzzy clustering and AR models for damage detection in CFRP coupons considering loading effect
Cano, Wagner Francisco Rezende
AR models
Composite materials
Damage detection
Fuzzy clustering
Lamb waves
Load variations
Smart Structures
title_short Fuzzy clustering and AR models for damage detection in CFRP coupons considering loading effect
title_full Fuzzy clustering and AR models for damage detection in CFRP coupons considering loading effect
title_fullStr Fuzzy clustering and AR models for damage detection in CFRP coupons considering loading effect
title_full_unstemmed Fuzzy clustering and AR models for damage detection in CFRP coupons considering loading effect
title_sort Fuzzy clustering and AR models for damage detection in CFRP coupons considering loading effect
author Cano, Wagner Francisco Rezende
author_facet Cano, Wagner Francisco Rezende
da Silva, Samuel [UNESP]
author_role author
author2 da Silva, Samuel [UNESP]
author2_role author
dc.contributor.none.fl_str_mv Fundação Centro de Pesquisa e Desenvolvimento em Telecomunicações - CPqD
Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Cano, Wagner Francisco Rezende
da Silva, Samuel [UNESP]
dc.subject.por.fl_str_mv AR models
Composite materials
Damage detection
Fuzzy clustering
Lamb waves
Load variations
Smart Structures
topic AR models
Composite materials
Damage detection
Fuzzy clustering
Lamb waves
Load variations
Smart Structures
description This paper proposes a strategy to avoid false alarms by distinguishing operation effects from damages effects in composite laminates. This strategy is based on active and sensing piezoelectric patches receiving Lamb waves that can be profoundly affected by operational factors such as load leading to false diagnostics. In order to overcome this drawback, this paper proposes an approach analyzing the use of prediction errors obtained by auto-regressive (AR) models. This index is computed using only the output signal received from sensors and combined with other traditional sensitive-damage indices. The fuzzy clustering technique is then applied for distinguishing the load effects from the effects of the damage. The method is evaluated using a carbon fiber-reinforced polymer coupons subject to tension–tension fatigue and with layers of piezoelectric sensors and actuators bonded on this surface. The results revealed that fuzzy clustering using a fuzzy c-means (FCM) algorithm could distinguish these effects using one-step-ahead AR errors combined with other standard indices extracted in time and frequency domains. This strategy may be easily implemented for signal processing, making possible its online application in a real-world structure.
publishDate 2020
dc.date.none.fl_str_mv 2020-12-12T01:21:13Z
2020-12-12T01:21:13Z
2020-05-01
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/10.1007/s40430-020-02304-7
Journal of the Brazilian Society of Mechanical Sciences and Engineering, v. 42, n. 5, 2020.
1806-3691
1678-5878
http://hdl.handle.net/11449/198756
10.1007/s40430-020-02304-7
2-s2.0-85083645813
url http://dx.doi.org/10.1007/s40430-020-02304-7
http://hdl.handle.net/11449/198756
identifier_str_mv Journal of the Brazilian Society of Mechanical Sciences and Engineering, v. 42, n. 5, 2020.
1806-3691
1678-5878
10.1007/s40430-020-02304-7
2-s2.0-85083645813
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
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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_ 1808128927171870720