Long-range correlations and natural time series analyses from acoustic emission signals
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/237316 |
Resumo: | This work focuses on analyzing acoustic emission (AE) signals as a means to predict failure in structures. There are two main approaches that are considered: (i) long-range correlation analysis using both the Hurst (H) and the detrended fluctuation analysis (DFA) exponents, and (ii) natural time domain (NT) analysis. These methodologies are applied to the data that were collected from two application examples: a glass fiber-reinforced polymeric plate and a spaghetti bridge model, where both structures were subjected to increasing loads until collapse. A traditional (AE) signal analysis was also performed to reference the study of the other methods. The results indicate that the proposed methods yield reliable indication of failure in the studied structures. |
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Friedrich, Leandro FerreiraCezar, Édiblu SilvaColpo, Angélica BordinTanzi, Boris Nahuel RojoSobczyk Sobrinho, Mario RolandLacidogna, GiuseppeNiccolini, GianniKosteski, Luis EduardoIturrioz, Ignacio2022-04-15T04:43:19Z20222076-3417http://hdl.handle.net/10183/237316001138326This work focuses on analyzing acoustic emission (AE) signals as a means to predict failure in structures. There are two main approaches that are considered: (i) long-range correlation analysis using both the Hurst (H) and the detrended fluctuation analysis (DFA) exponents, and (ii) natural time domain (NT) analysis. These methodologies are applied to the data that were collected from two application examples: a glass fiber-reinforced polymeric plate and a spaghetti bridge model, where both structures were subjected to increasing loads until collapse. A traditional (AE) signal analysis was also performed to reference the study of the other methods. The results indicate that the proposed methods yield reliable indication of failure in the studied structures.application/pdfengApplied sciences [recurso eletrônico]. Basel. Vol. 12, n. 4 (2022), Art. 1980, 23 p.Emissão acústicaDano estruturalAcoustic emissionLong-range correlationsNatural time analysisHeterogeneous materialsLong-range correlations and natural time series analyses from acoustic emission signalsEstrangeiroinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT001138326.pdf.txt001138326.pdf.txtExtracted Texttext/plain78929http://www.lume.ufrgs.br/bitstream/10183/237316/2/001138326.pdf.txt87025b7f7e05b7b315239e9026833733MD52ORIGINAL001138326.pdfTexto completo (inglês)application/pdf8968705http://www.lume.ufrgs.br/bitstream/10183/237316/1/001138326.pdf6280612c53adbb68255cb748caf8c04bMD5110183/2373162022-04-20 04:49:58.572oai:www.lume.ufrgs.br:10183/237316Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2022-04-20T07:49:58Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
Long-range correlations and natural time series analyses from acoustic emission signals |
title |
Long-range correlations and natural time series analyses from acoustic emission signals |
spellingShingle |
Long-range correlations and natural time series analyses from acoustic emission signals Friedrich, Leandro Ferreira Emissão acústica Dano estrutural Acoustic emission Long-range correlations Natural time analysis Heterogeneous materials |
title_short |
Long-range correlations and natural time series analyses from acoustic emission signals |
title_full |
Long-range correlations and natural time series analyses from acoustic emission signals |
title_fullStr |
Long-range correlations and natural time series analyses from acoustic emission signals |
title_full_unstemmed |
Long-range correlations and natural time series analyses from acoustic emission signals |
title_sort |
Long-range correlations and natural time series analyses from acoustic emission signals |
author |
Friedrich, Leandro Ferreira |
author_facet |
Friedrich, Leandro Ferreira Cezar, Édiblu Silva Colpo, Angélica Bordin Tanzi, Boris Nahuel Rojo Sobczyk Sobrinho, Mario Roland Lacidogna, Giuseppe Niccolini, Gianni Kosteski, Luis Eduardo Iturrioz, Ignacio |
author_role |
author |
author2 |
Cezar, Édiblu Silva Colpo, Angélica Bordin Tanzi, Boris Nahuel Rojo Sobczyk Sobrinho, Mario Roland Lacidogna, Giuseppe Niccolini, Gianni Kosteski, Luis Eduardo Iturrioz, Ignacio |
author2_role |
author author author author author author author author |
dc.contributor.author.fl_str_mv |
Friedrich, Leandro Ferreira Cezar, Édiblu Silva Colpo, Angélica Bordin Tanzi, Boris Nahuel Rojo Sobczyk Sobrinho, Mario Roland Lacidogna, Giuseppe Niccolini, Gianni Kosteski, Luis Eduardo Iturrioz, Ignacio |
dc.subject.por.fl_str_mv |
Emissão acústica Dano estrutural |
topic |
Emissão acústica Dano estrutural Acoustic emission Long-range correlations Natural time analysis Heterogeneous materials |
dc.subject.eng.fl_str_mv |
Acoustic emission Long-range correlations Natural time analysis Heterogeneous materials |
description |
This work focuses on analyzing acoustic emission (AE) signals as a means to predict failure in structures. There are two main approaches that are considered: (i) long-range correlation analysis using both the Hurst (H) and the detrended fluctuation analysis (DFA) exponents, and (ii) natural time domain (NT) analysis. These methodologies are applied to the data that were collected from two application examples: a glass fiber-reinforced polymeric plate and a spaghetti bridge model, where both structures were subjected to increasing loads until collapse. A traditional (AE) signal analysis was also performed to reference the study of the other methods. The results indicate that the proposed methods yield reliable indication of failure in the studied structures. |
publishDate |
2022 |
dc.date.accessioned.fl_str_mv |
2022-04-15T04:43:19Z |
dc.date.issued.fl_str_mv |
2022 |
dc.type.driver.fl_str_mv |
Estrangeiro 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://hdl.handle.net/10183/237316 |
dc.identifier.issn.pt_BR.fl_str_mv |
2076-3417 |
dc.identifier.nrb.pt_BR.fl_str_mv |
001138326 |
identifier_str_mv |
2076-3417 001138326 |
url |
http://hdl.handle.net/10183/237316 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
Applied sciences [recurso eletrônico]. Basel. Vol. 12, n. 4 (2022), Art. 1980, 23 p. |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFRGS instname:Universidade Federal do Rio Grande do Sul (UFRGS) instacron:UFRGS |
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UFRGS |
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Repositório Institucional da UFRGS |
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