Predictive maintenance mechanisms for heating equipment
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10773/29723 |
Resumo: | Heating appliances such as HVAC systems are susceptible to failures that may result in disruption of important operations. With this in mind, it is relevant to increase the efficiency of those solutions and diminish the number of detected faults. Moreover, understand why these failures occur that be relevant for future devices. Thus, there is a need to develop methods that allow the identification of eventual failures before they occur. This is only achievable when solutions capable of analyzing data, interpret it and obtaining knowledge from it, are created. This dissertation presents an infrastructure that supports the inspection of failure detection in boilers, making viable to forecast faults and errors. A major part of the work is data analysis and the creation of procedures that can process it. The main goal is creating an efficient system able to identify, predict and notify the occurrence of failure events. |
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Predictive maintenance mechanisms for heating equipmentBig Data applicationsMachine LearningHVACPredictive MaintenanceData processingData AnalysisHeating appliances such as HVAC systems are susceptible to failures that may result in disruption of important operations. With this in mind, it is relevant to increase the efficiency of those solutions and diminish the number of detected faults. Moreover, understand why these failures occur that be relevant for future devices. Thus, there is a need to develop methods that allow the identification of eventual failures before they occur. This is only achievable when solutions capable of analyzing data, interpret it and obtaining knowledge from it, are created. This dissertation presents an infrastructure that supports the inspection of failure detection in boilers, making viable to forecast faults and errors. A major part of the work is data analysis and the creation of procedures that can process it. The main goal is creating an efficient system able to identify, predict and notify the occurrence of failure events.Equipamentos de Climatização, como caldeiras e ar-condicionado, são suscetíveis a falhas que podem resultar na interrupção de operações importantes. Assim, é relevante aumentar a eficiência dessas soluções e diminuir o número de falhas detectadas. Além disso, entender o porquê da ocorrências dessas falhas torna-se importante para a criação de equipamentos futuros. Existe, assim, a necessidade de desenvolver métodos que permitam a identificação de eventuais falhas antes que elas ocorram. Isso só é possível quando são criadas soluções capazes de analisar dados, interpretá-los e obter conhecimento a partir deles. Esta dissertação apresenta uma infraestrutura que suporta a inspeção de detecção de falhas em caldeiras, viabilizando a previsão de falhas e erros. Uma parte importante do trabalho é a análise de dados e a criação de procedimentos que possam processá-los. O objetivo principal é criar um sistema eficiente capaz de identificar, prever e notificar a ocorrência de eventos de falha.2020-11-05T11:43:34Z2019-07-01T00:00:00Z2019-07info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10773/29723engSantiago, Ana Rita Antunesinfo: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:RCAAP2024-05-06T04:28:24Zoai:ria.ua.pt:10773/29723Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-06T04:28:24Repositório 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 |
Predictive maintenance mechanisms for heating equipment |
title |
Predictive maintenance mechanisms for heating equipment |
spellingShingle |
Predictive maintenance mechanisms for heating equipment Santiago, Ana Rita Antunes Big Data applications Machine Learning HVAC Predictive Maintenance Data processing Data Analysis |
title_short |
Predictive maintenance mechanisms for heating equipment |
title_full |
Predictive maintenance mechanisms for heating equipment |
title_fullStr |
Predictive maintenance mechanisms for heating equipment |
title_full_unstemmed |
Predictive maintenance mechanisms for heating equipment |
title_sort |
Predictive maintenance mechanisms for heating equipment |
author |
Santiago, Ana Rita Antunes |
author_facet |
Santiago, Ana Rita Antunes |
author_role |
author |
dc.contributor.author.fl_str_mv |
Santiago, Ana Rita Antunes |
dc.subject.por.fl_str_mv |
Big Data applications Machine Learning HVAC Predictive Maintenance Data processing Data Analysis |
topic |
Big Data applications Machine Learning HVAC Predictive Maintenance Data processing Data Analysis |
description |
Heating appliances such as HVAC systems are susceptible to failures that may result in disruption of important operations. With this in mind, it is relevant to increase the efficiency of those solutions and diminish the number of detected faults. Moreover, understand why these failures occur that be relevant for future devices. Thus, there is a need to develop methods that allow the identification of eventual failures before they occur. This is only achievable when solutions capable of analyzing data, interpret it and obtaining knowledge from it, are created. This dissertation presents an infrastructure that supports the inspection of failure detection in boilers, making viable to forecast faults and errors. A major part of the work is data analysis and the creation of procedures that can process it. The main goal is creating an efficient system able to identify, predict and notify the occurrence of failure events. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-07-01T00:00:00Z 2019-07 2020-11-05T11:43:34Z |
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/10773/29723 |
url |
http://hdl.handle.net/10773/29723 |
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 |
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1817543758365851648 |