Cloud computing based unsupervised fault diagnosis system in the context of Industry 4.0

Detalhes bibliográficos
Autor(a) principal: Ali,Amr Mohamed
Data de Publicação: 2020
Outros Autores: Mohamed,El-Adl, Yacout,Soumaya, Shaban,Yasser
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Gestão & Produção
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-530X2020000300310
Resumo: Abstract: New online fault monitoring and alarm systems, with the aid of Cyber-Physical Systems (CPS) and Cloud Technology (CT), are examined in this article within the context of Industry 4.0. The data collected from machines is used to implement maintenance strategies based on the diagnosis and prognosis of the machines' performance. As such, the purpose of this paper is to propose a Cloud Computing Platform containing three layers of technologies forming a Cyber-Physical System which receives unlabelled data to generate an interpreted online decision for the local team, as well as collecting historical data to improve the analyzer. The proposed troubleshooter is tested using unlabelled experimental data sets of rolling element bearing. Finally, the current and future Fault Diagnosis Systems and Cloud Technologies applications in the maintenance field are discussed.
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spelling Cloud computing based unsupervised fault diagnosis system in the context of Industry 4.0Remote Fault Diagnosis System (RFDS)Logical Analysis of Data (LAD)Cyber-Physical System (CPS)Pattern recognitionIndustry 4.0Cloud computingAbstract: New online fault monitoring and alarm systems, with the aid of Cyber-Physical Systems (CPS) and Cloud Technology (CT), are examined in this article within the context of Industry 4.0. The data collected from machines is used to implement maintenance strategies based on the diagnosis and prognosis of the machines' performance. As such, the purpose of this paper is to propose a Cloud Computing Platform containing three layers of technologies forming a Cyber-Physical System which receives unlabelled data to generate an interpreted online decision for the local team, as well as collecting historical data to improve the analyzer. The proposed troubleshooter is tested using unlabelled experimental data sets of rolling element bearing. Finally, the current and future Fault Diagnosis Systems and Cloud Technologies applications in the maintenance field are discussed.Universidade Federal de São Carlos2020-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-530X2020000300310Gestão & Produção v.27 n.3 2020reponame:Gestão & Produçãoinstname:Universidade Federal de São Carlos (UFSCAR)instacron:UFSCAR10.1590/0104-530x5378-20info:eu-repo/semantics/openAccessAli,Amr MohamedMohamed,El-AdlYacout,SoumayaShaban,Yassereng2020-08-07T00:00:00Zoai:scielo:S0104-530X2020000300310Revistahttps://www.gestaoeproducao.com/PUBhttps://old.scielo.br/oai/scielo-oai.phpgp@dep.ufscar.br||revistagestaoemanalise@unichristus.edu.br1806-96490104-530Xopendoar:2020-08-07T00:00Gestão & Produção - Universidade Federal de São Carlos (UFSCAR)false
dc.title.none.fl_str_mv Cloud computing based unsupervised fault diagnosis system in the context of Industry 4.0
title Cloud computing based unsupervised fault diagnosis system in the context of Industry 4.0
spellingShingle Cloud computing based unsupervised fault diagnosis system in the context of Industry 4.0
Ali,Amr Mohamed
Remote Fault Diagnosis System (RFDS)
Logical Analysis of Data (LAD)
Cyber-Physical System (CPS)
Pattern recognition
Industry 4.0
Cloud computing
title_short Cloud computing based unsupervised fault diagnosis system in the context of Industry 4.0
title_full Cloud computing based unsupervised fault diagnosis system in the context of Industry 4.0
title_fullStr Cloud computing based unsupervised fault diagnosis system in the context of Industry 4.0
title_full_unstemmed Cloud computing based unsupervised fault diagnosis system in the context of Industry 4.0
title_sort Cloud computing based unsupervised fault diagnosis system in the context of Industry 4.0
author Ali,Amr Mohamed
author_facet Ali,Amr Mohamed
Mohamed,El-Adl
Yacout,Soumaya
Shaban,Yasser
author_role author
author2 Mohamed,El-Adl
Yacout,Soumaya
Shaban,Yasser
author2_role author
author
author
dc.contributor.author.fl_str_mv Ali,Amr Mohamed
Mohamed,El-Adl
Yacout,Soumaya
Shaban,Yasser
dc.subject.por.fl_str_mv Remote Fault Diagnosis System (RFDS)
Logical Analysis of Data (LAD)
Cyber-Physical System (CPS)
Pattern recognition
Industry 4.0
Cloud computing
topic Remote Fault Diagnosis System (RFDS)
Logical Analysis of Data (LAD)
Cyber-Physical System (CPS)
Pattern recognition
Industry 4.0
Cloud computing
description Abstract: New online fault monitoring and alarm systems, with the aid of Cyber-Physical Systems (CPS) and Cloud Technology (CT), are examined in this article within the context of Industry 4.0. The data collected from machines is used to implement maintenance strategies based on the diagnosis and prognosis of the machines' performance. As such, the purpose of this paper is to propose a Cloud Computing Platform containing three layers of technologies forming a Cyber-Physical System which receives unlabelled data to generate an interpreted online decision for the local team, as well as collecting historical data to improve the analyzer. The proposed troubleshooter is tested using unlabelled experimental data sets of rolling element bearing. Finally, the current and future Fault Diagnosis Systems and Cloud Technologies applications in the maintenance field are discussed.
publishDate 2020
dc.date.none.fl_str_mv 2020-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-530X2020000300310
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-530X2020000300310
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0104-530x5378-20
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Universidade Federal de São Carlos
publisher.none.fl_str_mv Universidade Federal de São Carlos
dc.source.none.fl_str_mv Gestão & Produção v.27 n.3 2020
reponame:Gestão & Produção
instname:Universidade Federal de São Carlos (UFSCAR)
instacron:UFSCAR
instname_str Universidade Federal de São Carlos (UFSCAR)
instacron_str UFSCAR
institution UFSCAR
reponame_str Gestão & Produção
collection Gestão & Produção
repository.name.fl_str_mv Gestão & Produção - Universidade Federal de São Carlos (UFSCAR)
repository.mail.fl_str_mv gp@dep.ufscar.br||revistagestaoemanalise@unichristus.edu.br
_version_ 1750118207602032640

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