Forecasting appliances failures: a machine-learning approach to predictive maintenance
Autor(a) principal: | |
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Data de Publicação: | 2020 |
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/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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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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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 |
dc.format.none.fl_str_mv |
application/pdf |
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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1817543745288011776 |