Prognosis and fault detection in agricultural tractors using numerical simulation and artificial immune system algorithms
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Research, Society and Development |
Texto Completo: | https://rsdjournal.org/index.php/rsd/article/view/11191 |
Resumo: | In view of the growing technological advance in agriculture, and with a view to promoting increased productivity and job security for individuals involved in the techno-agricultural evolution process, this article develops an inteligente diagnostic system, proposing artificial immunological algorithms, inspired by the Immune System Biological, to apply to the process of monitoring the structural integrity of an agricultural tractor and the consequent analysis of structural failures, under normal soil conditions and in the short term. For this, the detection of failures in structural integrity in agricultural tractors is obtained to capture data continuously for machine learning, so that a numerical model is created and fel, under the calculation of differential equations, in order to measure the displacements of the tractor at as the tractor speed parameters change and the distance between ground levels are interspersed and, thus, result in possible structural risk prognoses. Computationally, through the Octave software, the analysis, identification and classification of the obtained data is possible with the use of negative selection and clonal selection algorithms. The inspection of the tractor structure with a focus on better conservation is the main point of the study, and with the relevant quality and consistency of the methodology presented and resulting from the research, it allows to indicate whether the tractor is in normal conditions or shows signs of failure structural, because if there are risks, the failure can be identified. |
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Prognosis and fault detection in agricultural tractors using numerical simulation and artificial immune system algorithms Pronóstico y detección de fallas en tractores agrícolas mediante simulación numérica y algoritmos del sistema inmunológico artificialPrognose e detecção de falhas em tratores agrícolas utilizando simulação numérica e algoritmos de sistemas imunológicos artificiaisTrator agrícolaMonitoramento de integridade estruturalSeleção negativaSeleção clonal.Tractor agrícolaMonitoreo de integridad estructuralSelección negativaSelección clonal.Agricultural tractorStructural integrity monitoringNegative selectionClonal selection.In view of the growing technological advance in agriculture, and with a view to promoting increased productivity and job security for individuals involved in the techno-agricultural evolution process, this article develops an inteligente diagnostic system, proposing artificial immunological algorithms, inspired by the Immune System Biological, to apply to the process of monitoring the structural integrity of an agricultural tractor and the consequent analysis of structural failures, under normal soil conditions and in the short term. For this, the detection of failures in structural integrity in agricultural tractors is obtained to capture data continuously for machine learning, so that a numerical model is created and fel, under the calculation of differential equations, in order to measure the displacements of the tractor at as the tractor speed parameters change and the distance between ground levels are interspersed and, thus, result in possible structural risk prognoses. Computationally, through the Octave software, the analysis, identification and classification of the obtained data is possible with the use of negative selection and clonal selection algorithms. The inspection of the tractor structure with a focus on better conservation is the main point of the study, and with the relevant quality and consistency of the methodology presented and resulting from the research, it allows to indicate whether the tractor is in normal conditions or shows signs of failure structural, because if there are risks, the failure can be identified.Ante el creciente avance tecnológico en la agricultura, y con miras a promover una mayor productividad y seguridad laboral para las personas involucradas en el proceso de evolución tecnoagrícola, este artículo desarrolla un sistema de diagnóstico inteligente, proponiendo algoritmos inmunológicos artificiales, inspirados en el Sistema Inmunológico Biológico, para aplicar al proceso de monitoreo de la integridad estructural de un tractor agrícola y el consecuente análisis de fallas estructurales, en condiciones normales de suelo y en el corto plazo. Para ello, la detección de fallas en la integridad estructural en tractores agrícolas se obtiene para capturar datos de manera continua para el aprendizaje automático, de manera que se crea y alimenta un modelo numérico, bajo el cálculo de ecuaciones diferenciales, con el fin de medir los desplazamientos del tractor en a medida que los parámetros de velocidad del tractor cambian y la distancia entre los niveles del suelo se intercalan y, por lo tanto, resultan en posibles pronósticos de riesgo estructural. Computacionalmente, a través del software Octave, el análisis, identificación y clasificación de los datos obtenidos es posible con el uso de algoritmos de selección negativa y selección clonal. La inspección de la estructura del tractor con foco en una mejor conservación es el punto principal del estudio, y con la relevante calidad y consistencia de la metodología presentada y resultado de la investigación, permite indicar si el tractor se encuentra en condiciones normales o presenta signos de avería estructural, porque si hay riesgos, se puede identificar la falla.Diante o avanço tecnológico crescente na agricultura, e tendo em vista promover aumento da produtividade e segurança no trabalho dos indivíduos envolvidos no processo de evolução tecno-agrícola, o presente artigo propõe desenvolver um sistema inteligente de prognose e detecção de falhas aplicado ao processo de monitoramento da integridade estrutural de um trator agrícola e da consequente análise das falhas estruturais, em condições normais de solo e em curto prazo. Para isto, a detecção de falhas na integridade estrutural em tratores agrícolas é obtida para captar dados continuamente para aprendizado da máquina, de modo que um modelo numérico seja criado e alimentado, sob o cálculo de equações diferenciais, afim de mensurar os deslocamentos do trator à medida que os parâmetros de velocidade do trator se alteram e a distância entre aos níveis do solo se intercalam e, assim, resultar em possíveis prognósticos de risco estrutural. Computacionalmente, por via do software Octave, a análise, identificação e classificação dos dados obtidos, é possível com o uso de algoritmos de seleção negativa e seleção clonal. A inspeção da estrutura do trator com foco na melhor conservação do próprio é o ponto principal do estudo, e com a relevante qualidade e consistência da metodologia apresentada e resultante da pesquisa, permite indicar se o trator se encontra em condições normais ou apresenta indícios de falha estrutural, pois, caso haja riscos, a falha pode ser identificada.Research, Society and Development2020-12-23info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/1119110.33448/rsd-v9i12.11191Research, Society and Development; Vol. 9 No. 12; e31691211191Research, Society and Development; Vol. 9 Núm. 12; e31691211191Research, Society and Development; v. 9 n. 12; e316912111912525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIporhttps://rsdjournal.org/index.php/rsd/article/view/11191/9908Copyright (c) 2020 Dionatan Pontes de Oliveira; Wayrone Klaiton Silva; Daniela Cabral de Oliveira; Fábio Roberto Chavarette; Daniel Emanuel Cabral de Oliveira; Luis Cláudio Villani Ortiz ; Dorgival Fidellis de Souza; João Areis Ferreira Barbosa Júniorhttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessOliveira, Dionatan Pontes de Silva, Wayrone Klaiton Oliveira, Daniela Cabral deChavarette, Fábio Roberto Oliveira, Daniel Emanuel Cabral de Ortiz , Luis Cláudio Villani Souza, Dorgival Fidellis de Barbosa Júnior, João Areis Ferreira 2020-12-30T23:32:22Zoai:ojs.pkp.sfu.ca:article/11191Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:33:05.110958Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false |
dc.title.none.fl_str_mv |
Prognosis and fault detection in agricultural tractors using numerical simulation and artificial immune system algorithms Pronóstico y detección de fallas en tractores agrícolas mediante simulación numérica y algoritmos del sistema inmunológico artificial Prognose e detecção de falhas em tratores agrícolas utilizando simulação numérica e algoritmos de sistemas imunológicos artificiais |
title |
Prognosis and fault detection in agricultural tractors using numerical simulation and artificial immune system algorithms |
spellingShingle |
Prognosis and fault detection in agricultural tractors using numerical simulation and artificial immune system algorithms Oliveira, Dionatan Pontes de Trator agrícola Monitoramento de integridade estrutural Seleção negativa Seleção clonal. Tractor agrícola Monitoreo de integridad estructural Selección negativa Selección clonal. Agricultural tractor Structural integrity monitoring Negative selection Clonal selection. |
title_short |
Prognosis and fault detection in agricultural tractors using numerical simulation and artificial immune system algorithms |
title_full |
Prognosis and fault detection in agricultural tractors using numerical simulation and artificial immune system algorithms |
title_fullStr |
Prognosis and fault detection in agricultural tractors using numerical simulation and artificial immune system algorithms |
title_full_unstemmed |
Prognosis and fault detection in agricultural tractors using numerical simulation and artificial immune system algorithms |
title_sort |
Prognosis and fault detection in agricultural tractors using numerical simulation and artificial immune system algorithms |
author |
Oliveira, Dionatan Pontes de |
author_facet |
Oliveira, Dionatan Pontes de Silva, Wayrone Klaiton Oliveira, Daniela Cabral de Chavarette, Fábio Roberto Oliveira, Daniel Emanuel Cabral de Ortiz , Luis Cláudio Villani Souza, Dorgival Fidellis de Barbosa Júnior, João Areis Ferreira |
author_role |
author |
author2 |
Silva, Wayrone Klaiton Oliveira, Daniela Cabral de Chavarette, Fábio Roberto Oliveira, Daniel Emanuel Cabral de Ortiz , Luis Cláudio Villani Souza, Dorgival Fidellis de Barbosa Júnior, João Areis Ferreira |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Oliveira, Dionatan Pontes de Silva, Wayrone Klaiton Oliveira, Daniela Cabral de Chavarette, Fábio Roberto Oliveira, Daniel Emanuel Cabral de Ortiz , Luis Cláudio Villani Souza, Dorgival Fidellis de Barbosa Júnior, João Areis Ferreira |
dc.subject.por.fl_str_mv |
Trator agrícola Monitoramento de integridade estrutural Seleção negativa Seleção clonal. Tractor agrícola Monitoreo de integridad estructural Selección negativa Selección clonal. Agricultural tractor Structural integrity monitoring Negative selection Clonal selection. |
topic |
Trator agrícola Monitoramento de integridade estrutural Seleção negativa Seleção clonal. Tractor agrícola Monitoreo de integridad estructural Selección negativa Selección clonal. Agricultural tractor Structural integrity monitoring Negative selection Clonal selection. |
description |
In view of the growing technological advance in agriculture, and with a view to promoting increased productivity and job security for individuals involved in the techno-agricultural evolution process, this article develops an inteligente diagnostic system, proposing artificial immunological algorithms, inspired by the Immune System Biological, to apply to the process of monitoring the structural integrity of an agricultural tractor and the consequent analysis of structural failures, under normal soil conditions and in the short term. For this, the detection of failures in structural integrity in agricultural tractors is obtained to capture data continuously for machine learning, so that a numerical model is created and fel, under the calculation of differential equations, in order to measure the displacements of the tractor at as the tractor speed parameters change and the distance between ground levels are interspersed and, thus, result in possible structural risk prognoses. Computationally, through the Octave software, the analysis, identification and classification of the obtained data is possible with the use of negative selection and clonal selection algorithms. The inspection of the tractor structure with a focus on better conservation is the main point of the study, and with the relevant quality and consistency of the methodology presented and resulting from the research, it allows to indicate whether the tractor is in normal conditions or shows signs of failure structural, because if there are risks, the failure can be identified. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-23 |
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://rsdjournal.org/index.php/rsd/article/view/11191 10.33448/rsd-v9i12.11191 |
url |
https://rsdjournal.org/index.php/rsd/article/view/11191 |
identifier_str_mv |
10.33448/rsd-v9i12.11191 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/11191/9908 |
dc.rights.driver.fl_str_mv |
https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
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 |
Research, Society and Development |
publisher.none.fl_str_mv |
Research, Society and Development |
dc.source.none.fl_str_mv |
Research, Society and Development; Vol. 9 No. 12; e31691211191 Research, Society and Development; Vol. 9 Núm. 12; e31691211191 Research, Society and Development; v. 9 n. 12; e31691211191 2525-3409 reponame:Research, Society and Development instname:Universidade Federal de Itajubá (UNIFEI) instacron:UNIFEI |
instname_str |
Universidade Federal de Itajubá (UNIFEI) |
instacron_str |
UNIFEI |
institution |
UNIFEI |
reponame_str |
Research, Society and Development |
collection |
Research, Society and Development |
repository.name.fl_str_mv |
Research, Society and Development - Universidade Federal de Itajubá (UNIFEI) |
repository.mail.fl_str_mv |
rsd.articles@gmail.com |
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1797052782661861376 |