Predictive Maintenance Support System in Industry 4.0 Scenario
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/10216/132743 |
Resumo: | The fourth industrial revolution that is being witnessed nowadays, also known as Industry 4.0, is heavily related to the digitization of manufacturing systems and the integration of different technologies to optimize manufacturing. By combining data acquisition using specific sensors and machine learning algorithms to analyze this data and predict a failure before it happens, Predictive Maintenance is a critical tool to implement towards reducing downtime due to unpredicted stoppages caused by malfunctions. Based on the reality of Commercial Specialty Tires factory at Continental Mabor - Indústria de Pneus, S.A., the present work describes several problems faced regarding equipment maintenance. Taking advantage of the information gathered from studying the processes incorporated in the factory, it is designed a solution model for applying predictive maintenance in these processes. The model is divided into two primary layers, hardware, and software. Concerning hardware, sensors and respective applications are delineated. In terms of software, techniques of data analysis namely machine learning algorithms are described so that the collected data is studied to detect possible failures. |
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Predictive Maintenance Support System in Industry 4.0 ScenarioEngenharia electrotécnica, electrónica e informáticaElectrical engineering, Electronic engineering, Information engineeringThe fourth industrial revolution that is being witnessed nowadays, also known as Industry 4.0, is heavily related to the digitization of manufacturing systems and the integration of different technologies to optimize manufacturing. By combining data acquisition using specific sensors and machine learning algorithms to analyze this data and predict a failure before it happens, Predictive Maintenance is a critical tool to implement towards reducing downtime due to unpredicted stoppages caused by malfunctions. Based on the reality of Commercial Specialty Tires factory at Continental Mabor - Indústria de Pneus, S.A., the present work describes several problems faced regarding equipment maintenance. Taking advantage of the information gathered from studying the processes incorporated in the factory, it is designed a solution model for applying predictive maintenance in these processes. The model is divided into two primary layers, hardware, and software. Concerning hardware, sensors and respective applications are delineated. In terms of software, techniques of data analysis namely machine learning algorithms are described so that the collected data is studied to detect possible failures.2020-07-232020-07-23T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/10216/132743TID:202594785engRodrigo Ardachessian Costainfo: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:RCAAP2023-11-29T13:49:10Zoai:repositorio-aberto.up.pt:10216/132743Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:48:29.769464Repositório 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 Support System in Industry 4.0 Scenario |
title |
Predictive Maintenance Support System in Industry 4.0 Scenario |
spellingShingle |
Predictive Maintenance Support System in Industry 4.0 Scenario Rodrigo Ardachessian Costa Engenharia electrotécnica, electrónica e informática Electrical engineering, Electronic engineering, Information engineering |
title_short |
Predictive Maintenance Support System in Industry 4.0 Scenario |
title_full |
Predictive Maintenance Support System in Industry 4.0 Scenario |
title_fullStr |
Predictive Maintenance Support System in Industry 4.0 Scenario |
title_full_unstemmed |
Predictive Maintenance Support System in Industry 4.0 Scenario |
title_sort |
Predictive Maintenance Support System in Industry 4.0 Scenario |
author |
Rodrigo Ardachessian Costa |
author_facet |
Rodrigo Ardachessian Costa |
author_role |
author |
dc.contributor.author.fl_str_mv |
Rodrigo Ardachessian Costa |
dc.subject.por.fl_str_mv |
Engenharia electrotécnica, electrónica e informática Electrical engineering, Electronic engineering, Information engineering |
topic |
Engenharia electrotécnica, electrónica e informática Electrical engineering, Electronic engineering, Information engineering |
description |
The fourth industrial revolution that is being witnessed nowadays, also known as Industry 4.0, is heavily related to the digitization of manufacturing systems and the integration of different technologies to optimize manufacturing. By combining data acquisition using specific sensors and machine learning algorithms to analyze this data and predict a failure before it happens, Predictive Maintenance is a critical tool to implement towards reducing downtime due to unpredicted stoppages caused by malfunctions. Based on the reality of Commercial Specialty Tires factory at Continental Mabor - Indústria de Pneus, S.A., the present work describes several problems faced regarding equipment maintenance. Taking advantage of the information gathered from studying the processes incorporated in the factory, it is designed a solution model for applying predictive maintenance in these processes. The model is divided into two primary layers, hardware, and software. Concerning hardware, sensors and respective applications are delineated. In terms of software, techniques of data analysis namely machine learning algorithms are described so that the collected data is studied to detect possible failures. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-07-23 2020-07-23T00:00:00Z |
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/10216/132743 TID:202594785 |
url |
https://hdl.handle.net/10216/132743 |
identifier_str_mv |
TID:202594785 |
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 |
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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 |
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1799135803364868096 |