Forecasting appliances failures: a machine-learning approach to predictive maintenance

Detalhes bibliográficos
Autor(a) principal: Fernandes, Sofia
Data de Publicação: 2020
Outros Autores: Antunes, Mário, Santiago, Ana Rita, Barraca, João Paulo, Gomes, Diogo, Aguiar, Rui L.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10773/28657
Resumo: Heating appliances consume approximately 48% of the energy spent on household appliances every year. Furthermore, a malfunctioning device can increase the cost even further. Thus, there is a need to create methods that can identify the equipment’s malfunctions and eventual failures before they occur. This is only possible with a combination of data acquisition, analysis and prediction/forecast. This paper presents an infrastructure that supports the previously mentioned capabilities and was deployed for failure detection in boilers, making possible to forecast faults and errors. We also present our initial predictive maintenance models based on the collected data.
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spelling Forecasting appliances failures: a machine-learning approach to predictive maintenanceBig data applicationsBig data servicesInfrastructureData processingData analysisPredictive maintenanceMachine learningHeating appliances consume approximately 48% of the energy spent on household appliances every year. Furthermore, a malfunctioning device can increase the cost even further. Thus, there is a need to create methods that can identify the equipment’s malfunctions and eventual failures before they occur. This is only possible with a combination of data acquisition, analysis and prediction/forecast. This paper presents an infrastructure that supports the previously mentioned capabilities and was deployed for failure detection in boilers, making possible to forecast faults and errors. We also present our initial predictive maintenance models based on the collected data.MDPI2020-06-12T10:07:13Z2020-04-14T00:00:00Z2020-04-14info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/28657eng10.3390/info11040208Fernandes, SofiaAntunes, MárioSantiago, Ana RitaBarraca, João PauloGomes, DiogoAguiar, Rui L.info: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:RCAAP2024-05-06T04:26:08Zoai:ria.ua.pt:10773/28657Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-06T04:26:08Repositório 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 Forecasting appliances failures: a machine-learning approach to predictive maintenance
title Forecasting appliances failures: a machine-learning approach to predictive maintenance
spellingShingle Forecasting appliances failures: a machine-learning approach to predictive maintenance
Fernandes, Sofia
Big data applications
Big data services
Infrastructure
Data processing
Data analysis
Predictive maintenance
Machine learning
title_short Forecasting appliances failures: a machine-learning approach to predictive maintenance
title_full Forecasting appliances failures: a machine-learning approach to predictive maintenance
title_fullStr Forecasting appliances failures: a machine-learning approach to predictive maintenance
title_full_unstemmed Forecasting appliances failures: a machine-learning approach to predictive maintenance
title_sort Forecasting appliances failures: a machine-learning approach to predictive maintenance
author Fernandes, Sofia
author_facet Fernandes, Sofia
Antunes, Mário
Santiago, Ana Rita
Barraca, João Paulo
Gomes, Diogo
Aguiar, Rui L.
author_role author
author2 Antunes, Mário
Santiago, Ana Rita
Barraca, João Paulo
Gomes, Diogo
Aguiar, Rui L.
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Fernandes, Sofia
Antunes, Mário
Santiago, Ana Rita
Barraca, João Paulo
Gomes, Diogo
Aguiar, Rui L.
dc.subject.por.fl_str_mv Big data applications
Big data services
Infrastructure
Data processing
Data analysis
Predictive maintenance
Machine learning
topic Big data applications
Big data services
Infrastructure
Data processing
Data analysis
Predictive maintenance
Machine learning
description Heating appliances consume approximately 48% of the energy spent on household appliances every year. Furthermore, a malfunctioning device can increase the cost even further. Thus, there is a need to create methods that can identify the equipment’s malfunctions and eventual failures before they occur. This is only possible with a combination of data acquisition, analysis and prediction/forecast. This paper presents an infrastructure that supports the previously mentioned capabilities and was deployed for failure detection in boilers, making possible to forecast faults and errors. We also present our initial predictive maintenance models based on the collected data.
publishDate 2020
dc.date.none.fl_str_mv 2020-06-12T10:07:13Z
2020-04-14T00:00:00Z
2020-04-14
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/10773/28657
url http://hdl.handle.net/10773/28657
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.3390/info11040208
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
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
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