Application of monte carlo method for failure prediction: a tool to support maintenance management
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Revista Produção Online |
Texto Completo: | https://www.producaoonline.org.br/rpo/article/view/3091 |
Resumo: | The maintenance field has undergone important changes in the last decades, oriented, mainly, by the evolution of the managerial concepts in the companies. Although it has been treated for long periods as an onerous sector for organizations, maintenance is pointed out in the latest literature as a huge source of competitiveness. To explore its potential, however, Maintenance Management must incorporate Engineering as the driving force for routine processes and improvements. In a practical way, the company must work to avoid failures or, at least, to foresee them. In line with this strategic vision, the present work engages in the development and validation of a failure prediction system. Using the Monte Carlo Method, this article integrates a quantitative study of modeling and simulation. From mathematical and statistical concepts, different failure prediction series were formulated and comparative analyses were performed on their precisions. As results, it was verified the effectiveness of the method in determining the moment of occurrence of failures from numerical simulations and evidenced the optimal regions of prediction of each proposed series. Among the main contributions of the study, we highlight the higher precision of the series simulated by the Monte Carlo Method in relation to the series estimated from the historical average of the data, despite the good adjustment of these series in selected areas of the real curve. Future works will investigate the behavior of other models of a series of failures, generated from new combinations of the proposed parameters. |
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Application of monte carlo method for failure prediction: a tool to support maintenance managementAplicação do método de Monte Carlo para a previsão de falhas: uma ferramenta de apoio à gestão da manutençãoMaintenance Engineering. Monte Carlo Simulation. Failures Prediction. Applied Statistics.Engenharia de Manutenção. Simulação de Monte Carlo. Previsão de Falhas. Estatística Aplicada.The maintenance field has undergone important changes in the last decades, oriented, mainly, by the evolution of the managerial concepts in the companies. Although it has been treated for long periods as an onerous sector for organizations, maintenance is pointed out in the latest literature as a huge source of competitiveness. To explore its potential, however, Maintenance Management must incorporate Engineering as the driving force for routine processes and improvements. In a practical way, the company must work to avoid failures or, at least, to foresee them. In line with this strategic vision, the present work engages in the development and validation of a failure prediction system. Using the Monte Carlo Method, this article integrates a quantitative study of modeling and simulation. From mathematical and statistical concepts, different failure prediction series were formulated and comparative analyses were performed on their precisions. As results, it was verified the effectiveness of the method in determining the moment of occurrence of failures from numerical simulations and evidenced the optimal regions of prediction of each proposed series. Among the main contributions of the study, we highlight the higher precision of the series simulated by the Monte Carlo Method in relation to the series estimated from the historical average of the data, despite the good adjustment of these series in selected areas of the real curve. Future works will investigate the behavior of other models of a series of failures, generated from new combinations of the proposed parameters.A área de manutenção tem passado por mudanças importantes nas últimas décadas, orientada, principalmente, pela evolução dos conceitos gerenciais nas empresas. Embora tenha sido tratada, por longos períodos, como um setor oneroso para as organizações, a manutenção é apontada, na literatura mais recente, como uma enorme fonte de competitividade. Para explorar seu potencial, no entanto, a Gestão da Manutenção deve incorporar a Engenharia como o agente direcionador dos processos de rotina e melhorias. De forma prática, a empresa deve trabalhar para evitar as falhas ou, no mínimo, prevê-las. Alinhado a essa visão estratégica, o presente trabalho empenha-se no desenvolvimento e validação de um sistema de previsão de falhas. Utilizando-se do Método de Monte Carlo, este artigo integra um estudo de natureza quantitativa do gênero modelagem e simulação. A partir de conceitos matemáticos e estatísticos, foram formuladas diferentes séries de previsão de falhas e realizadas análises comparativas de suas precisões. Como resultados, foi constatada a eficácia do método na determinação do momento de ocorrência de falhas a partir de simulações numéricas e evidenciadas as regiões ótimas de previsão de cada série proposta. Dentre as principais contribuições do trabalho, destaca-se a maior precisão das séries simuladas pelo Método de Monte Carlo em relação às séries estimadas a partir da média histórica dos dados, apesar do bom ajuste dessas séries em áreas selecionadas da curva real. Trabalhos futuros irão investigar o comportamento de outros modelos de séries de falhas, geradas a partir de novas combinações dos parâmetros propostos.Associação Brasileira de Engenharia de Produção2019-03-15info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfvideo/mp4https://www.producaoonline.org.br/rpo/article/view/309110.14488/1676-1901.v19i1.3091Revista Produção Online; Vol. 19 No. 1 (2019); 72-101Revista Produção Online; v. 19 n. 1 (2019); 72-1011676-1901reponame:Revista Produção Onlineinstname:Associação Brasileira de Engenharia de Produção (ABEPRO)instacron:ABEPROporhttps://www.producaoonline.org.br/rpo/article/view/3091/1754https://www.producaoonline.org.br/rpo/article/view/3091/1755Copyright (c) 2019 Revista Produção Onlineinfo:eu-repo/semantics/openAccessde Oliveira, Lucas GuedesPaiva, Emerson José dePaiva, Anderson Paulo de2019-03-15T10:27:51Zoai:ojs.emnuvens.com.br:article/3091Revistahttp://producaoonline.org.br/rpoPUBhttps://www.producaoonline.org.br/rpo/oai||producaoonline@gmail.com1676-19011676-1901opendoar:2019-03-15T10:27:51Revista Produção Online - Associação Brasileira de Engenharia de Produção (ABEPRO)false |
dc.title.none.fl_str_mv |
Application of monte carlo method for failure prediction: a tool to support maintenance management Aplicação do método de Monte Carlo para a previsão de falhas: uma ferramenta de apoio à gestão da manutenção |
title |
Application of monte carlo method for failure prediction: a tool to support maintenance management |
spellingShingle |
Application of monte carlo method for failure prediction: a tool to support maintenance management de Oliveira, Lucas Guedes Maintenance Engineering. Monte Carlo Simulation. Failures Prediction. Applied Statistics. Engenharia de Manutenção. Simulação de Monte Carlo. Previsão de Falhas. Estatística Aplicada. |
title_short |
Application of monte carlo method for failure prediction: a tool to support maintenance management |
title_full |
Application of monte carlo method for failure prediction: a tool to support maintenance management |
title_fullStr |
Application of monte carlo method for failure prediction: a tool to support maintenance management |
title_full_unstemmed |
Application of monte carlo method for failure prediction: a tool to support maintenance management |
title_sort |
Application of monte carlo method for failure prediction: a tool to support maintenance management |
author |
de Oliveira, Lucas Guedes |
author_facet |
de Oliveira, Lucas Guedes Paiva, Emerson José de Paiva, Anderson Paulo de |
author_role |
author |
author2 |
Paiva, Emerson José de Paiva, Anderson Paulo de |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
de Oliveira, Lucas Guedes Paiva, Emerson José de Paiva, Anderson Paulo de |
dc.subject.por.fl_str_mv |
Maintenance Engineering. Monte Carlo Simulation. Failures Prediction. Applied Statistics. Engenharia de Manutenção. Simulação de Monte Carlo. Previsão de Falhas. Estatística Aplicada. |
topic |
Maintenance Engineering. Monte Carlo Simulation. Failures Prediction. Applied Statistics. Engenharia de Manutenção. Simulação de Monte Carlo. Previsão de Falhas. Estatística Aplicada. |
description |
The maintenance field has undergone important changes in the last decades, oriented, mainly, by the evolution of the managerial concepts in the companies. Although it has been treated for long periods as an onerous sector for organizations, maintenance is pointed out in the latest literature as a huge source of competitiveness. To explore its potential, however, Maintenance Management must incorporate Engineering as the driving force for routine processes and improvements. In a practical way, the company must work to avoid failures or, at least, to foresee them. In line with this strategic vision, the present work engages in the development and validation of a failure prediction system. Using the Monte Carlo Method, this article integrates a quantitative study of modeling and simulation. From mathematical and statistical concepts, different failure prediction series were formulated and comparative analyses were performed on their precisions. As results, it was verified the effectiveness of the method in determining the moment of occurrence of failures from numerical simulations and evidenced the optimal regions of prediction of each proposed series. Among the main contributions of the study, we highlight the higher precision of the series simulated by the Monte Carlo Method in relation to the series estimated from the historical average of the data, despite the good adjustment of these series in selected areas of the real curve. Future works will investigate the behavior of other models of a series of failures, generated from new combinations of the proposed parameters. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-03-15 |
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://www.producaoonline.org.br/rpo/article/view/3091 10.14488/1676-1901.v19i1.3091 |
url |
https://www.producaoonline.org.br/rpo/article/view/3091 |
identifier_str_mv |
10.14488/1676-1901.v19i1.3091 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://www.producaoonline.org.br/rpo/article/view/3091/1754 https://www.producaoonline.org.br/rpo/article/view/3091/1755 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2019 Revista Produção Online info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2019 Revista Produção Online |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf video/mp4 |
dc.publisher.none.fl_str_mv |
Associação Brasileira de Engenharia de Produção |
publisher.none.fl_str_mv |
Associação Brasileira de Engenharia de Produção |
dc.source.none.fl_str_mv |
Revista Produção Online; Vol. 19 No. 1 (2019); 72-101 Revista Produção Online; v. 19 n. 1 (2019); 72-101 1676-1901 reponame:Revista Produção Online instname:Associação Brasileira de Engenharia de Produção (ABEPRO) instacron:ABEPRO |
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Associação Brasileira de Engenharia de Produção (ABEPRO) |
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ABEPRO |
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ABEPRO |
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Revista Produção Online |
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Revista Produção Online |
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Revista Produção Online - Associação Brasileira de Engenharia de Produção (ABEPRO) |
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||producaoonline@gmail.com |
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