A reliability engineering case study of sugarcane harvesters
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Gestão & Produção |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-530X2020000400201 |
Resumo: | Abstract: The present study aimed to analyze factors associated with the equipment failures of the sugarcane harvester, whose machineries has high importance in the harvest process and cost involved. Part of the data was originally provided by a company located in the countryside of Sao Paulo State, from two machines, collected from January 2015 to August 2017, corresponding to 2.5 crops. The overall dataset was obtained from three different sources: a stop-tracking system, which provides the track of a preventive and corrective maintenance historical of the analyzed equipment; telemetry data of the equipment, captured through embedded computer systems, installed in the machine’ type under study, which provide information on its operation; and meteorological data from the Brazilian National Institute of Meteorology. Multivariate analyzes were used such as principal components and multiple regression models, therefore creating a model for prediction considering the next equipment’ break, then pointing to causes of process failures. Thus, the results point to some improvements concerned with individualized reliability scheme in order to reduce the number of corrective stops given the equipment. |
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Gestão & Produção |
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A reliability engineering case study of sugarcane harvestersReliabilityMultivariate analysisOptimization in maintenance planningAbstract: The present study aimed to analyze factors associated with the equipment failures of the sugarcane harvester, whose machineries has high importance in the harvest process and cost involved. Part of the data was originally provided by a company located in the countryside of Sao Paulo State, from two machines, collected from January 2015 to August 2017, corresponding to 2.5 crops. The overall dataset was obtained from three different sources: a stop-tracking system, which provides the track of a preventive and corrective maintenance historical of the analyzed equipment; telemetry data of the equipment, captured through embedded computer systems, installed in the machine’ type under study, which provide information on its operation; and meteorological data from the Brazilian National Institute of Meteorology. Multivariate analyzes were used such as principal components and multiple regression models, therefore creating a model for prediction considering the next equipment’ break, then pointing to causes of process failures. Thus, the results point to some improvements concerned with individualized reliability scheme in order to reduce the number of corrective stops given the equipment.Universidade Federal de São Carlos2020-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-530X2020000400201Gestão & Produção v.27 n.4 2020reponame:Gestão & Produçãoinstname:Universidade Federal de São Carlos (UFSCAR)instacron:UFSCAR10.1590/0104-530x4569-20info:eu-repo/semantics/openAccessNascimento,Diego Carvalho doRamos,Pedro LuizEnnes,AndréCocolo,CamilaNicola,Márcio JoséAlonso,CarlosRibeiro,Luiz GustavoLouzada,Franciscoeng2020-07-22T00:00:00Zoai:scielo:S0104-530X2020000400201Revistahttps://www.gestaoeproducao.com/PUBhttps://old.scielo.br/oai/scielo-oai.phpgp@dep.ufscar.br||revistagestaoemanalise@unichristus.edu.br1806-96490104-530Xopendoar:2020-07-22T00:00Gestão & Produção - Universidade Federal de São Carlos (UFSCAR)false |
dc.title.none.fl_str_mv |
A reliability engineering case study of sugarcane harvesters |
title |
A reliability engineering case study of sugarcane harvesters |
spellingShingle |
A reliability engineering case study of sugarcane harvesters Nascimento,Diego Carvalho do Reliability Multivariate analysis Optimization in maintenance planning |
title_short |
A reliability engineering case study of sugarcane harvesters |
title_full |
A reliability engineering case study of sugarcane harvesters |
title_fullStr |
A reliability engineering case study of sugarcane harvesters |
title_full_unstemmed |
A reliability engineering case study of sugarcane harvesters |
title_sort |
A reliability engineering case study of sugarcane harvesters |
author |
Nascimento,Diego Carvalho do |
author_facet |
Nascimento,Diego Carvalho do Ramos,Pedro Luiz Ennes,André Cocolo,Camila Nicola,Márcio José Alonso,Carlos Ribeiro,Luiz Gustavo Louzada,Francisco |
author_role |
author |
author2 |
Ramos,Pedro Luiz Ennes,André Cocolo,Camila Nicola,Márcio José Alonso,Carlos Ribeiro,Luiz Gustavo Louzada,Francisco |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Nascimento,Diego Carvalho do Ramos,Pedro Luiz Ennes,André Cocolo,Camila Nicola,Márcio José Alonso,Carlos Ribeiro,Luiz Gustavo Louzada,Francisco |
dc.subject.por.fl_str_mv |
Reliability Multivariate analysis Optimization in maintenance planning |
topic |
Reliability Multivariate analysis Optimization in maintenance planning |
description |
Abstract: The present study aimed to analyze factors associated with the equipment failures of the sugarcane harvester, whose machineries has high importance in the harvest process and cost involved. Part of the data was originally provided by a company located in the countryside of Sao Paulo State, from two machines, collected from January 2015 to August 2017, corresponding to 2.5 crops. The overall dataset was obtained from three different sources: a stop-tracking system, which provides the track of a preventive and corrective maintenance historical of the analyzed equipment; telemetry data of the equipment, captured through embedded computer systems, installed in the machine’ type under study, which provide information on its operation; and meteorological data from the Brazilian National Institute of Meteorology. Multivariate analyzes were used such as principal components and multiple regression models, therefore creating a model for prediction considering the next equipment’ break, then pointing to causes of process failures. Thus, the results point to some improvements concerned with individualized reliability scheme in order to reduce the number of corrective stops given the equipment. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-01-01 |
dc.type.driver.fl_str_mv |
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://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-530X2020000400201 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-530X2020000400201 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0104-530x4569-20 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Universidade Federal de São Carlos |
publisher.none.fl_str_mv |
Universidade Federal de São Carlos |
dc.source.none.fl_str_mv |
Gestão & Produção v.27 n.4 2020 reponame:Gestão & Produção instname:Universidade Federal de São Carlos (UFSCAR) instacron:UFSCAR |
instname_str |
Universidade Federal de São Carlos (UFSCAR) |
instacron_str |
UFSCAR |
institution |
UFSCAR |
reponame_str |
Gestão & Produção |
collection |
Gestão & Produção |
repository.name.fl_str_mv |
Gestão & Produção - Universidade Federal de São Carlos (UFSCAR) |
repository.mail.fl_str_mv |
gp@dep.ufscar.br||revistagestaoemanalise@unichristus.edu.br |
_version_ |
1750118207607275520 |