A reliability engineering case study of sugarcane harvesters

Detalhes bibliográficos
Autor(a) principal: Nascimento,Diego Carvalho do
Data de Publicação: 2020
Outros Autores: Ramos,Pedro Luiz, Ennes,André, Cocolo,Camila, Nicola,Márcio José, Alonso,Carlos, Ribeiro,Luiz Gustavo, Louzada,Francisco
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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spelling 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
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