Data-driven autoregressive model identification for structural health monitoring in anisotropic composite plates
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/205994 |
Resumo: | A simple data-driven AutoRegressive (AR) model may be used to assess a model to describe and to predict the time-series outputs of the PZT sensors receiving Lamb waves for different operating conditions in composite structures. Thus, this paper presents the potentiality of the use of a set of AR models to detect, locate, and, manly, to extrapolate a damage sensitive index based on changes in one-step-ahead prediction errors. To illustrate this proposal, an aeronautical composite panel with bonded piezoelectric elements, that act both as sensors and actuators, is used to study the relationship between the variation of the parameters of the identified model and the presence of various simulated damage. A damage progression evaluation by extrapolating the AR parameters is also suggested and examined based on cubic spline functions to verify the future state and to observe how the damage could evolute, based on some simplified assumptions. This step could help to make a decision about a possible required repair without adopting a complicated and costly physical model. |
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Repositório Institucional da UNESP |
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Data-driven autoregressive model identification for structural health monitoring in anisotropic composite platesAR ModelsExtrapolated ModelMultiple ModelsPrognosisQuantificationA simple data-driven AutoRegressive (AR) model may be used to assess a model to describe and to predict the time-series outputs of the PZT sensors receiving Lamb waves for different operating conditions in composite structures. Thus, this paper presents the potentiality of the use of a set of AR models to detect, locate, and, manly, to extrapolate a damage sensitive index based on changes in one-step-ahead prediction errors. To illustrate this proposal, an aeronautical composite panel with bonded piezoelectric elements, that act both as sensors and actuators, is used to study the relationship between the variation of the parameters of the identified model and the presence of various simulated damage. A damage progression evaluation by extrapolating the AR parameters is also suggested and examined based on cubic spline functions to verify the future state and to observe how the damage could evolute, based on some simplified assumptions. This step could help to make a decision about a possible required repair without adopting a complicated and costly physical model.Departamento de Engenharia Mecânica Universidade Estadual Paulista - UNESP, Av. Brasil 56PIMM Laboratory ENSAM/CNRS/CNAM, 151 Boulevard de l’HôpitalDepartamento de Engenharia Mecânica Universidade Estadual Paulista - UNESP, Av. Brasil 56Universidade Estadual Paulista (Unesp)ENSAM/CNRS/CNAMSilva, Samuel D.A. [UNESP]Paixão, Jessé [UNESP]Rébillat, MarcMechbal, Nazih2021-06-25T10:24:47Z2021-06-25T10:24:47Z2019-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject1213-12239th ECCOMAS Thematic Conference on Smart Structures and Materials, SMART 2019, p. 1213-1223.http://hdl.handle.net/11449/2059942-s2.0-85101981419Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng9th ECCOMAS Thematic Conference on Smart Structures and Materials, SMART 2019info:eu-repo/semantics/openAccess2021-10-22T20:28:44Zoai:repositorio.unesp.br:11449/205994Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T13:34:57.470118Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Data-driven autoregressive model identification for structural health monitoring in anisotropic composite plates |
title |
Data-driven autoregressive model identification for structural health monitoring in anisotropic composite plates |
spellingShingle |
Data-driven autoregressive model identification for structural health monitoring in anisotropic composite plates Silva, Samuel D.A. [UNESP] AR Models Extrapolated Model Multiple Models Prognosis Quantification |
title_short |
Data-driven autoregressive model identification for structural health monitoring in anisotropic composite plates |
title_full |
Data-driven autoregressive model identification for structural health monitoring in anisotropic composite plates |
title_fullStr |
Data-driven autoregressive model identification for structural health monitoring in anisotropic composite plates |
title_full_unstemmed |
Data-driven autoregressive model identification for structural health monitoring in anisotropic composite plates |
title_sort |
Data-driven autoregressive model identification for structural health monitoring in anisotropic composite plates |
author |
Silva, Samuel D.A. [UNESP] |
author_facet |
Silva, Samuel D.A. [UNESP] Paixão, Jessé [UNESP] Rébillat, Marc Mechbal, Nazih |
author_role |
author |
author2 |
Paixão, Jessé [UNESP] Rébillat, Marc Mechbal, Nazih |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) ENSAM/CNRS/CNAM |
dc.contributor.author.fl_str_mv |
Silva, Samuel D.A. [UNESP] Paixão, Jessé [UNESP] Rébillat, Marc Mechbal, Nazih |
dc.subject.por.fl_str_mv |
AR Models Extrapolated Model Multiple Models Prognosis Quantification |
topic |
AR Models Extrapolated Model Multiple Models Prognosis Quantification |
description |
A simple data-driven AutoRegressive (AR) model may be used to assess a model to describe and to predict the time-series outputs of the PZT sensors receiving Lamb waves for different operating conditions in composite structures. Thus, this paper presents the potentiality of the use of a set of AR models to detect, locate, and, manly, to extrapolate a damage sensitive index based on changes in one-step-ahead prediction errors. To illustrate this proposal, an aeronautical composite panel with bonded piezoelectric elements, that act both as sensors and actuators, is used to study the relationship between the variation of the parameters of the identified model and the presence of various simulated damage. A damage progression evaluation by extrapolating the AR parameters is also suggested and examined based on cubic spline functions to verify the future state and to observe how the damage could evolute, based on some simplified assumptions. This step could help to make a decision about a possible required repair without adopting a complicated and costly physical model. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-01-01 2021-06-25T10:24:47Z 2021-06-25T10:24:47Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
9th ECCOMAS Thematic Conference on Smart Structures and Materials, SMART 2019, p. 1213-1223. http://hdl.handle.net/11449/205994 2-s2.0-85101981419 |
identifier_str_mv |
9th ECCOMAS Thematic Conference on Smart Structures and Materials, SMART 2019, p. 1213-1223. 2-s2.0-85101981419 |
url |
http://hdl.handle.net/11449/205994 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
9th ECCOMAS Thematic Conference on Smart Structures and Materials, SMART 2019 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1213-1223 |
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_ |
1808128249401704448 |