An architecture to predict anomalies in industrial processes

Detalhes bibliográficos
Autor(a) principal: Dias, Filipe Miguel Machado
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/150593
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
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spelling An architecture to predict anomalies in industrial processesPredictive MaintenanceMachine LearningIndustrial Internet of ThingsRemaining Useful LifeDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe Internet of Things (IoT) and machine learning algorithms (ML) are enabling a revolutionary change in digitization in numerous areas, benefiting Industry 4.0 in particular. Predictive maintenance using machine learning models is being used to protect assets in industry. In this paper, an architecture for predicting anomalies in industrial processes was proposed in which SMEs can be guided in implementing an IIoT architecture for predictive maintenance (PdM). This research was conducted to understand what machine learning architectures and models are generally used by industry for PdM. An overview of the concepts of the Industrial Internet of Things (IIoT), machine learning (ML), and predictive maintenance (PdM) was provided, and through a systematic literature review, it was possible to understand their applications and which technologies enable their use. The survey revealed that PdM applications are increasingly common and that there are many studies on the development of new ML techniques. The survey conducted confirmed the usefulness of the artifact and showed the need for an architecture to guide the implementation of PdM. This research can be a contribution for SMEs, allowing them to become more efficient and reduce both production and maintenance costs in order to keep up with multinational companies.Santos, Vitor Manuel Pereira Duarte dosRUNDias, Filipe Miguel Machado2023-03-15T17:43:24Z2023-01-272023-01-27T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/150593TID:203247388enginfo: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:RCAAP2024-05-22T18:10:00Zoai:run.unl.pt:10362/150593Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-22T18:10Repositório 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 An architecture to predict anomalies in industrial processes
title An architecture to predict anomalies in industrial processes
spellingShingle An architecture to predict anomalies in industrial processes
Dias, Filipe Miguel Machado
Predictive Maintenance
Machine Learning
Industrial Internet of Things
Remaining Useful Life
title_short An architecture to predict anomalies in industrial processes
title_full An architecture to predict anomalies in industrial processes
title_fullStr An architecture to predict anomalies in industrial processes
title_full_unstemmed An architecture to predict anomalies in industrial processes
title_sort An architecture to predict anomalies in industrial processes
author Dias, Filipe Miguel Machado
author_facet Dias, Filipe Miguel Machado
author_role author
dc.contributor.none.fl_str_mv Santos, Vitor Manuel Pereira Duarte dos
RUN
dc.contributor.author.fl_str_mv Dias, Filipe Miguel Machado
dc.subject.por.fl_str_mv Predictive Maintenance
Machine Learning
Industrial Internet of Things
Remaining Useful Life
topic Predictive Maintenance
Machine Learning
Industrial Internet of Things
Remaining Useful Life
description Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
publishDate 2023
dc.date.none.fl_str_mv 2023-03-15T17:43:24Z
2023-01-27
2023-01-27T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/150593
TID:203247388
url http://hdl.handle.net/10362/150593
identifier_str_mv TID:203247388
dc.language.iso.fl_str_mv eng
language eng
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.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 mluisa.alvim@gmail.com
_version_ 1817545923331358720

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