Failure prognosis in a piston flow reactor of the tubular reactor using artificial immune systems
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | The Journal of Engineering and Exact Sciences |
Texto Completo: | https://periodicos.ufv.br/jcec/article/view/12366 |
Resumo: | Industry 4.0 uses principles of new technologies to achieve better quality and productivity results, which intelligent computing is one of them. In this work is proposed an intelligent methodology in which the artificial immune system sequesters the signal into groups and determines the classification based on failure prognose by the degree of severity of a tubular reactor with piston flow. The process is developed as follows: basically, after obtaining the vibration signals from the reactor through a numerical model, the Fourier rapid transform is used to transform the signals in the frequency domain. Later, a negatively select artificial immune system performs the diagnosis, identifying and classifying failures. The motivation for the application of this methodology is the process of supervision of structures, in order to identify and characterize failures, as well as make decisions aimed at avoiding accidents or disasters. The results demonstrate the accuracy and the robustness of the methodology for the reactor operation. |
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Failure prognosis in a piston flow reactor of the tubular reactor using artificial immune systemsPrognose de falhas em um reator do reator tubular com escoamento pistonado utilizando sistemas imunológicos artificiais Sistemas Imunológicos ArtificiaisReatorMonitoramentoArtificial Immune Systems. Reactor. Reliability. SHM. Monitoring.Industry 4.0 uses principles of new technologies to achieve better quality and productivity results, which intelligent computing is one of them. In this work is proposed an intelligent methodology in which the artificial immune system sequesters the signal into groups and determines the classification based on failure prognose by the degree of severity of a tubular reactor with piston flow. The process is developed as follows: basically, after obtaining the vibration signals from the reactor through a numerical model, the Fourier rapid transform is used to transform the signals in the frequency domain. Later, a negatively select artificial immune system performs the diagnosis, identifying and classifying failures. The motivation for the application of this methodology is the process of supervision of structures, in order to identify and characterize failures, as well as make decisions aimed at avoiding accidents or disasters. The results demonstrate the accuracy and the robustness of the methodology for the reactor operation.A indústria 4.0 utiliza princípios de novas tecnologias para obter resultados de melhor qualidade e produtividade, o qual a computação inteligente é uma delas. Neste trabalho é proposto uma metodologia inteligente em que o sistema imunológico artificial separa o sinal em grupamentos e determina a classificação baseado em prognose de falhas pelo grau de severidade de um reator tubular com escoamento pistonado. O processo é desenvolvido da seguinte forma: basicamente, após a obtenção dos sinais de vibração do reator através de um modelo numérico, é utilizada a transformada rápida de Fourier para transformar os sinais no domínio da frequência. Posteriormente, um sistema imunológico artificial de seleção negativa realiza o diagnóstico, identificando e classificando as falhas. A motivação da aplicação desta metodologia é o processo de fiscalização de estruturas, a fim de identificar e caracterizar as falhas, bem como tomar decisões visando evitar acidentes ou desastres. Os resultados demonstram a robustez e precisão da metodologia para a aplicação proposta.Universidade Federal de Viçosa - UFV2021-05-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.ufv.br/jcec/article/view/1236610.18540/jcecvl7iss2pp12366-01-10eThe Journal of Engineering and Exact Sciences; Vol. 7 No. 2 (2021); 12366-01-10eThe Journal of Engineering and Exact Sciences; Vol. 7 Núm. 2 (2021); 12366-01-10eThe Journal of Engineering and Exact Sciences; v. 7 n. 2 (2021); 12366-01-10e2527-1075reponame:The Journal of Engineering and Exact Sciencesinstname:Universidade Federal de Viçosa (UFV)instacron:UFVporhttps://periodicos.ufv.br/jcec/article/view/12366/6605Copyright (c) 2021 The Journal of Engineering and Exact Scienceshttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessChavarette, Fabio RobertoOuta, RobertoGonçalves, Aparecido Carlos2021-07-05T18:18:27Zoai:ojs.periodicos.ufv.br:article/12366Revistahttp://www.seer.ufv.br/seer/rbeq2/index.php/req2/oai2527-10752527-1075opendoar:2021-07-05T18:18:27The Journal of Engineering and Exact Sciences - Universidade Federal de Viçosa (UFV)false |
dc.title.none.fl_str_mv |
Failure prognosis in a piston flow reactor of the tubular reactor using artificial immune systems Prognose de falhas em um reator do reator tubular com escoamento pistonado utilizando sistemas imunológicos artificiais |
title |
Failure prognosis in a piston flow reactor of the tubular reactor using artificial immune systems |
spellingShingle |
Failure prognosis in a piston flow reactor of the tubular reactor using artificial immune systems Chavarette, Fabio Roberto Sistemas Imunológicos Artificiais Reator Monitoramento Artificial Immune Systems. Reactor. Reliability. SHM. Monitoring. |
title_short |
Failure prognosis in a piston flow reactor of the tubular reactor using artificial immune systems |
title_full |
Failure prognosis in a piston flow reactor of the tubular reactor using artificial immune systems |
title_fullStr |
Failure prognosis in a piston flow reactor of the tubular reactor using artificial immune systems |
title_full_unstemmed |
Failure prognosis in a piston flow reactor of the tubular reactor using artificial immune systems |
title_sort |
Failure prognosis in a piston flow reactor of the tubular reactor using artificial immune systems |
author |
Chavarette, Fabio Roberto |
author_facet |
Chavarette, Fabio Roberto Outa, Roberto Gonçalves, Aparecido Carlos |
author_role |
author |
author2 |
Outa, Roberto Gonçalves, Aparecido Carlos |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Chavarette, Fabio Roberto Outa, Roberto Gonçalves, Aparecido Carlos |
dc.subject.por.fl_str_mv |
Sistemas Imunológicos Artificiais Reator Monitoramento Artificial Immune Systems. Reactor. Reliability. SHM. Monitoring. |
topic |
Sistemas Imunológicos Artificiais Reator Monitoramento Artificial Immune Systems. Reactor. Reliability. SHM. Monitoring. |
description |
Industry 4.0 uses principles of new technologies to achieve better quality and productivity results, which intelligent computing is one of them. In this work is proposed an intelligent methodology in which the artificial immune system sequesters the signal into groups and determines the classification based on failure prognose by the degree of severity of a tubular reactor with piston flow. The process is developed as follows: basically, after obtaining the vibration signals from the reactor through a numerical model, the Fourier rapid transform is used to transform the signals in the frequency domain. Later, a negatively select artificial immune system performs the diagnosis, identifying and classifying failures. The motivation for the application of this methodology is the process of supervision of structures, in order to identify and characterize failures, as well as make decisions aimed at avoiding accidents or disasters. The results demonstrate the accuracy and the robustness of the methodology for the reactor operation. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-05-04 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://periodicos.ufv.br/jcec/article/view/12366 10.18540/jcecvl7iss2pp12366-01-10e |
url |
https://periodicos.ufv.br/jcec/article/view/12366 |
identifier_str_mv |
10.18540/jcecvl7iss2pp12366-01-10e |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://periodicos.ufv.br/jcec/article/view/12366/6605 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2021 The Journal of Engineering and Exact Sciences https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2021 The Journal of Engineering and Exact Sciences https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Viçosa - UFV |
publisher.none.fl_str_mv |
Universidade Federal de Viçosa - UFV |
dc.source.none.fl_str_mv |
The Journal of Engineering and Exact Sciences; Vol. 7 No. 2 (2021); 12366-01-10e The Journal of Engineering and Exact Sciences; Vol. 7 Núm. 2 (2021); 12366-01-10e The Journal of Engineering and Exact Sciences; v. 7 n. 2 (2021); 12366-01-10e 2527-1075 reponame:The Journal of Engineering and Exact Sciences instname:Universidade Federal de Viçosa (UFV) instacron:UFV |
instname_str |
Universidade Federal de Viçosa (UFV) |
instacron_str |
UFV |
institution |
UFV |
reponame_str |
The Journal of Engineering and Exact Sciences |
collection |
The Journal of Engineering and Exact Sciences |
repository.name.fl_str_mv |
The Journal of Engineering and Exact Sciences - Universidade Federal de Viçosa (UFV) |
repository.mail.fl_str_mv |
|
_version_ |
1808845246226759680 |