Anomaly Detection on Multivariate Time Series from CNC Machining using Machine Learning techniques

Detalhes bibliográficos
Autor(a) principal: Gabriel Copolecchia Carvalhal
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/10216/153047
Resumo: The industry 4.0 paradigm paves the way that manufacturing factories develop their real-time monitoring capabilities. In this context, this study investigates machine learning applications in real-world data from CNC machining sensors in Jasil, Portugal, for an anomaly detection task as industrial quality control. In order to improve the availability of systems, reduce maintenance costs, increase operational performance, and support decision-making.
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spelling Anomaly Detection on Multivariate Time Series from CNC Machining using Machine Learning techniquesOutras ciências da engenharia e tecnologiasOther engineering and technologiesThe industry 4.0 paradigm paves the way that manufacturing factories develop their real-time monitoring capabilities. In this context, this study investigates machine learning applications in real-world data from CNC machining sensors in Jasil, Portugal, for an anomaly detection task as industrial quality control. In order to improve the availability of systems, reduce maintenance costs, increase operational performance, and support decision-making.2023-09-262023-09-26T00:00:00Z2026-09-25T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/10216/153047TID:203423518engGabriel Copolecchia Carvalhalinfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-12-22T01:35:10Zoai:repositorio-aberto.up.pt:10216/153047Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:34:27.904711Repositório 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 Anomaly Detection on Multivariate Time Series from CNC Machining using Machine Learning techniques
title Anomaly Detection on Multivariate Time Series from CNC Machining using Machine Learning techniques
spellingShingle Anomaly Detection on Multivariate Time Series from CNC Machining using Machine Learning techniques
Gabriel Copolecchia Carvalhal
Outras ciências da engenharia e tecnologias
Other engineering and technologies
title_short Anomaly Detection on Multivariate Time Series from CNC Machining using Machine Learning techniques
title_full Anomaly Detection on Multivariate Time Series from CNC Machining using Machine Learning techniques
title_fullStr Anomaly Detection on Multivariate Time Series from CNC Machining using Machine Learning techniques
title_full_unstemmed Anomaly Detection on Multivariate Time Series from CNC Machining using Machine Learning techniques
title_sort Anomaly Detection on Multivariate Time Series from CNC Machining using Machine Learning techniques
author Gabriel Copolecchia Carvalhal
author_facet Gabriel Copolecchia Carvalhal
author_role author
dc.contributor.author.fl_str_mv Gabriel Copolecchia Carvalhal
dc.subject.por.fl_str_mv Outras ciências da engenharia e tecnologias
Other engineering and technologies
topic Outras ciências da engenharia e tecnologias
Other engineering and technologies
description The industry 4.0 paradigm paves the way that manufacturing factories develop their real-time monitoring capabilities. In this context, this study investigates machine learning applications in real-world data from CNC machining sensors in Jasil, Portugal, for an anomaly detection task as industrial quality control. In order to improve the availability of systems, reduce maintenance costs, increase operational performance, and support decision-making.
publishDate 2023
dc.date.none.fl_str_mv 2023-09-26
2023-09-26T00:00:00Z
2026-09-25T00:00:00Z
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 https://hdl.handle.net/10216/153047
TID:203423518
url https://hdl.handle.net/10216/153047
identifier_str_mv TID:203423518
dc.language.iso.fl_str_mv eng
language eng
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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
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