Short and long forecast to implement predictive maintenance in a pulp industry
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/100596 https://doi.org/10.17531/ein.2022.1.5 |
Resumo: | Predictive maintenance is very important for effective prevention of failures in an industry. The present paper describes a case study where a wood chip pump system was analyzed, and a predictive model was proposed. An Ishikawa diagram and FMECA are used to identify possible causes for system failure. The Chip Wood has several sensors installed to monitor the working conditions and system state. The authors propose a variation of exponential smoothing technique for short time forecasting and an artificial neural network for long time forecasting. The algorithms were integrated into a dashboard for online condition monitoring, where the users are alerted when a variable is determined or predicted to get out of the expected range. Experimental results show prediction errors in general less than 10 %. The proposed technique may be of help in monitoring and maintenance of the asset, aiming at greater availability. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Short and long forecast to implement predictive maintenance in a pulp industrypredictive maintenancecondition based maintenancetime seriesartificial neural networksforecastingPredictive maintenance is very important for effective prevention of failures in an industry. The present paper describes a case study where a wood chip pump system was analyzed, and a predictive model was proposed. An Ishikawa diagram and FMECA are used to identify possible causes for system failure. The Chip Wood has several sensors installed to monitor the working conditions and system state. The authors propose a variation of exponential smoothing technique for short time forecasting and an artificial neural network for long time forecasting. The algorithms were integrated into a dashboard for online condition monitoring, where the users are alerted when a variable is determined or predicted to get out of the expected range. Experimental results show prediction errors in general less than 10 %. The proposed technique may be of help in monitoring and maintenance of the asset, aiming at greater availability.2021info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/100596http://hdl.handle.net/10316/100596https://doi.org/10.17531/ein.2022.1.5eng15072711Rodrigues, João AntunesFarinha, José Manuel TorresMendes, MateusMateus, RicardoCardoso, Antónioinfo: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:RCAAP2022-07-06T20:36:59Zoai:estudogeral.uc.pt:10316/100596Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:17:57.151561Repositório 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 |
Short and long forecast to implement predictive maintenance in a pulp industry |
title |
Short and long forecast to implement predictive maintenance in a pulp industry |
spellingShingle |
Short and long forecast to implement predictive maintenance in a pulp industry Rodrigues, João Antunes predictive maintenance condition based maintenance time series artificial neural networks forecasting |
title_short |
Short and long forecast to implement predictive maintenance in a pulp industry |
title_full |
Short and long forecast to implement predictive maintenance in a pulp industry |
title_fullStr |
Short and long forecast to implement predictive maintenance in a pulp industry |
title_full_unstemmed |
Short and long forecast to implement predictive maintenance in a pulp industry |
title_sort |
Short and long forecast to implement predictive maintenance in a pulp industry |
author |
Rodrigues, João Antunes |
author_facet |
Rodrigues, João Antunes Farinha, José Manuel Torres Mendes, Mateus Mateus, Ricardo Cardoso, António |
author_role |
author |
author2 |
Farinha, José Manuel Torres Mendes, Mateus Mateus, Ricardo Cardoso, António |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Rodrigues, João Antunes Farinha, José Manuel Torres Mendes, Mateus Mateus, Ricardo Cardoso, António |
dc.subject.por.fl_str_mv |
predictive maintenance condition based maintenance time series artificial neural networks forecasting |
topic |
predictive maintenance condition based maintenance time series artificial neural networks forecasting |
description |
Predictive maintenance is very important for effective prevention of failures in an industry. The present paper describes a case study where a wood chip pump system was analyzed, and a predictive model was proposed. An Ishikawa diagram and FMECA are used to identify possible causes for system failure. The Chip Wood has several sensors installed to monitor the working conditions and system state. The authors propose a variation of exponential smoothing technique for short time forecasting and an artificial neural network for long time forecasting. The algorithms were integrated into a dashboard for online condition monitoring, where the users are alerted when a variable is determined or predicted to get out of the expected range. Experimental results show prediction errors in general less than 10 %. The proposed technique may be of help in monitoring and maintenance of the asset, aiming at greater availability. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021 |
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/10316/100596 http://hdl.handle.net/10316/100596 https://doi.org/10.17531/ein.2022.1.5 |
url |
http://hdl.handle.net/10316/100596 https://doi.org/10.17531/ein.2022.1.5 |
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 |
repository.mail.fl_str_mv |
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1799134074950909952 |