Semi-Double-loop machine learning based CPS approach for predictive maintenance in manufacturing system based on machine status indications

Detalhes bibliográficos
Autor(a) principal: Putnik, Goran D.
Data de Publicação: 2021
Outros Autores: Manupati, Vijaya Kumar, Pabba, Sai Krishna, Varela, M.L.R., Ferreira, Francisco
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/1822/77995
Resumo: The paper presents two original and innovative contributions: 1) the model of machine learning (ML) based approach for predictive maintenance in manufacturing system based on machine status indications only, and 2) semi-Double-loop machine learning based intelligent Cyber-Physical System (I-CPS) architecture as a higher-level environment for ML based predictive maintenance execution. Considering only the machine status information provides rapid and very low investment-based implementation of an advanced predictive maintenance paradigm, especially important for SMEs. The model is validated in real-life situations, exploring different learning algorithms and strategies for learning maintenance predictive models. The findings show very high level of prediction accuracy.
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spelling Semi-Double-loop machine learning based CPS approach for predictive maintenance in manufacturing system based on machine status indicationsManufacturing systemPredictive maintenanceMaintenanceScience & TechnologyThe paper presents two original and innovative contributions: 1) the model of machine learning (ML) based approach for predictive maintenance in manufacturing system based on machine status indications only, and 2) semi-Double-loop machine learning based intelligent Cyber-Physical System (I-CPS) architecture as a higher-level environment for ML based predictive maintenance execution. Considering only the machine status information provides rapid and very low investment-based implementation of an advanced predictive maintenance paradigm, especially important for SMEs. The model is validated in real-life situations, exploring different learning algorithms and strategies for learning maintenance predictive models. The findings show very high level of prediction accuracy.This work has been supported by FCT – Fundação para a Ciência e Tecnologia, Portugal, within the Project Scope: UIDB/00319/2020.ElsevierUniversidade do MinhoPutnik, Goran D.Manupati, Vijaya KumarPabba, Sai KrishnaVarela, M.L.R.Ferreira, Francisco20212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/77995engPutnik, G. D., Manupati, V. K., Pabba, S. K., Varela, L., & Ferreira, F. (2021). Semi-Double-loop machine learning based CPS approach for predictive maintenance in manufacturing system based on machine status indications. CIRP Annals, 70(1), 365-368. doi: https://doi.org/10.1016/j.cirp.2021.04.0460007-850610.1016/j.cirp.2021.04.046https://www.sciencedirect.com/science/article/pii/S0007850621000706info: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:RCAAP2023-07-21T12:11:23Zoai:repositorium.sdum.uminho.pt:1822/77995Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:03:09.615838Repositório 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 Semi-Double-loop machine learning based CPS approach for predictive maintenance in manufacturing system based on machine status indications
title Semi-Double-loop machine learning based CPS approach for predictive maintenance in manufacturing system based on machine status indications
spellingShingle Semi-Double-loop machine learning based CPS approach for predictive maintenance in manufacturing system based on machine status indications
Putnik, Goran D.
Manufacturing system
Predictive maintenance
Maintenance
Science & Technology
title_short Semi-Double-loop machine learning based CPS approach for predictive maintenance in manufacturing system based on machine status indications
title_full Semi-Double-loop machine learning based CPS approach for predictive maintenance in manufacturing system based on machine status indications
title_fullStr Semi-Double-loop machine learning based CPS approach for predictive maintenance in manufacturing system based on machine status indications
title_full_unstemmed Semi-Double-loop machine learning based CPS approach for predictive maintenance in manufacturing system based on machine status indications
title_sort Semi-Double-loop machine learning based CPS approach for predictive maintenance in manufacturing system based on machine status indications
author Putnik, Goran D.
author_facet Putnik, Goran D.
Manupati, Vijaya Kumar
Pabba, Sai Krishna
Varela, M.L.R.
Ferreira, Francisco
author_role author
author2 Manupati, Vijaya Kumar
Pabba, Sai Krishna
Varela, M.L.R.
Ferreira, Francisco
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Putnik, Goran D.
Manupati, Vijaya Kumar
Pabba, Sai Krishna
Varela, M.L.R.
Ferreira, Francisco
dc.subject.por.fl_str_mv Manufacturing system
Predictive maintenance
Maintenance
Science & Technology
topic Manufacturing system
Predictive maintenance
Maintenance
Science & Technology
description The paper presents two original and innovative contributions: 1) the model of machine learning (ML) based approach for predictive maintenance in manufacturing system based on machine status indications only, and 2) semi-Double-loop machine learning based intelligent Cyber-Physical System (I-CPS) architecture as a higher-level environment for ML based predictive maintenance execution. Considering only the machine status information provides rapid and very low investment-based implementation of an advanced predictive maintenance paradigm, especially important for SMEs. The model is validated in real-life situations, exploring different learning algorithms and strategies for learning maintenance predictive models. The findings show very high level of prediction accuracy.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-01-01T00:00:00Z
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 https://hdl.handle.net/1822/77995
url https://hdl.handle.net/1822/77995
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Putnik, G. D., Manupati, V. K., Pabba, S. K., Varela, L., & Ferreira, F. (2021). Semi-Double-loop machine learning based CPS approach for predictive maintenance in manufacturing system based on machine status indications. CIRP Annals, 70(1), 365-368. doi: https://doi.org/10.1016/j.cirp.2021.04.046
0007-8506
10.1016/j.cirp.2021.04.046
https://www.sciencedirect.com/science/article/pii/S0007850621000706
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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
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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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