Predicting motor oil condition using artificial neural networks and principal component analysis

Detalhes bibliográficos
Autor(a) principal: Rodrigues, João
Data de Publicação: 2020
Outros Autores: Costa, Inês, Farinha, J. Torres, Mendes, Mateus, Margalho, Luís
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10316/101256
https://doi.org/10.17531/ein.2020.3.6
Resumo: The safety and performance of engines such as Diesel, gas or even wind turbines depends on the quality and condition of the lubricant oil. Assessment of engine oil condition is done based on more than twenty variables that have, individually, variations that depend on the engines’ behaviour, type and other factors. The present paper describes a model to automatically classify the oil condition, using Artificial Neural Networks and Principal Component Analysis. The study was done using data obtained from two passenger bus companies in a country of Southern Europe. The results show the importance of each variable monitored for determining the ideal time to change oil. In many cases, it may be possible to enlarge intervals between maintenance interventions, while in other cases the oil passed the ideal change point.
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spelling Predicting motor oil condition using artificial neural networks and principal component analysiscondition monitoringoil analysismultivariate analysispredictive maintenancemonitorowanie stanuanaliza olejuanaliza wielowymiarowakonserwacja predykcyjnaThe safety and performance of engines such as Diesel, gas or even wind turbines depends on the quality and condition of the lubricant oil. Assessment of engine oil condition is done based on more than twenty variables that have, individually, variations that depend on the engines’ behaviour, type and other factors. The present paper describes a model to automatically classify the oil condition, using Artificial Neural Networks and Principal Component Analysis. The study was done using data obtained from two passenger bus companies in a country of Southern Europe. The results show the importance of each variable monitored for determining the ideal time to change oil. In many cases, it may be possible to enlarge intervals between maintenance interventions, while in other cases the oil passed the ideal change point.Bezpieczeństwo i wydajność silników takich, jak silniki Diesla czy gazowe, a nawet turbiny wiatrowe, zależą od jakości i stanu oleju smarowego. Stanu oleju silnikowego ocenia się na podstawie ponad dwudziestu zmiennych, z których każda ulega wahaniom w zależności od typu i zachowania silnika oraz innych czynników. W niniejszym artykule opisano model, który pozwala na automatyczną klasyfikację stanu oleju, z wykorzystaniem sztucznych sieci neuronowych i analizy składowych głównych. Badania przeprowadzono na podstawie danych uzyskanych od dwóch przewoźników pasażerskich działających na terenie jednego z krajów położonych na południu Europy. Wyniki pokazują, że każda z monitorowanych zmiennych ma znaczenie dla określenia idealnego czasu na wymianę oleju. Podczas gdy w wielu przypadkach w badanych przedsiębiorstwach możliwe było zwiększenie odstępów czasowych między działaniami konserwacyjnymi, w innych, idealny moment wymiany oleju został przekroczony.2020info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/101256http://hdl.handle.net/10316/101256https://doi.org/10.17531/ein.2020.3.6eng15072711Rodrigues, JoãoCosta, InêsFarinha, J. TorresMendes, MateusMargalho, Luísinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-08-18T20:43:39Zoai:estudogeral.uc.pt:10316/101256Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:18:29.382566Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Predicting motor oil condition using artificial neural networks and principal component analysis
title Predicting motor oil condition using artificial neural networks and principal component analysis
spellingShingle Predicting motor oil condition using artificial neural networks and principal component analysis
Rodrigues, João
condition monitoring
oil analysis
multivariate analysis
predictive maintenance
monitorowanie stanu
analiza oleju
analiza wielowymiarowa
konserwacja predykcyjna
title_short Predicting motor oil condition using artificial neural networks and principal component analysis
title_full Predicting motor oil condition using artificial neural networks and principal component analysis
title_fullStr Predicting motor oil condition using artificial neural networks and principal component analysis
title_full_unstemmed Predicting motor oil condition using artificial neural networks and principal component analysis
title_sort Predicting motor oil condition using artificial neural networks and principal component analysis
author Rodrigues, João
author_facet Rodrigues, João
Costa, Inês
Farinha, J. Torres
Mendes, Mateus
Margalho, Luís
author_role author
author2 Costa, Inês
Farinha, J. Torres
Mendes, Mateus
Margalho, Luís
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Rodrigues, João
Costa, Inês
Farinha, J. Torres
Mendes, Mateus
Margalho, Luís
dc.subject.por.fl_str_mv condition monitoring
oil analysis
multivariate analysis
predictive maintenance
monitorowanie stanu
analiza oleju
analiza wielowymiarowa
konserwacja predykcyjna
topic condition monitoring
oil analysis
multivariate analysis
predictive maintenance
monitorowanie stanu
analiza oleju
analiza wielowymiarowa
konserwacja predykcyjna
description The safety and performance of engines such as Diesel, gas or even wind turbines depends on the quality and condition of the lubricant oil. Assessment of engine oil condition is done based on more than twenty variables that have, individually, variations that depend on the engines’ behaviour, type and other factors. The present paper describes a model to automatically classify the oil condition, using Artificial Neural Networks and Principal Component Analysis. The study was done using data obtained from two passenger bus companies in a country of Southern Europe. The results show the importance of each variable monitored for determining the ideal time to change oil. In many cases, it may be possible to enlarge intervals between maintenance interventions, while in other cases the oil passed the ideal change point.
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10316/101256
http://hdl.handle.net/10316/101256
https://doi.org/10.17531/ein.2020.3.6
url http://hdl.handle.net/10316/101256
https://doi.org/10.17531/ein.2020.3.6
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 15072711
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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