Tip4.0

Detalhes bibliográficos
Autor(a) principal: Resende, Carlos
Data de Publicação: 2021
Outros Autores: Folgado, Duarte, Oliveira, João, Franco, Bernardo, Moreira, Waldir, Oliveira-Jr, Antonio, Cavaleiro, Armando, Carvalho, Ricardo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/145960
Resumo: POCI-01-0247-FEDER-038436
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spelling Tip4.0Industrial internet of things platform for predictive maintenanceArtificial intelligenceEdge computingIndustry 4.0Internet of thingsPredictive maintenanceAnalytical ChemistryInformation SystemsAtomic and Molecular Physics, and OpticsBiochemistryInstrumentationElectrical and Electronic EngineeringSDG 9 - Industry, Innovation, and InfrastructureSDG 16 - Peace, Justice and Strong InstitutionsPOCI-01-0247-FEDER-038436Industry 4.0, allied with the growth and democratization of Artificial Intelligence (AI) and the advent of IoT, is paving the way for the complete digitization and automation of industrial processes. Maintenance is one of these processes, where the introduction of a predictive approach, as opposed to the traditional techniques, is expected to considerably improve the industry maintenance strategies with gains such as reduced downtime, improved equipment effectiveness, lower maintenance costs, increased return on assets, risk mitigation, and, ultimately, profitable growth. With predictive maintenance, dedicated sensors monitor the critical points of assets. The sensor data then feed into machine learning algorithms that can infer the asset health status and inform operators and decision-makers. With this in mind, in this paper, we present TIP4.0, a platform for predictive maintenance based on a modular software solution for edge computing gateways. TIP4.0 is built around Yocto, which makes it readily available and compliant with Commercial Off-the-Shelf (COTS) or proprietary hardware. TIP4.0 was conceived with an industry mindset with communication interfaces that allow it to serve sensor networks in the shop floor and modular software architecture that allows it to be easily adjusted to new deployment scenarios. To showcase its potential, the TIP4.0 platform was validated over COTS hardware, and we considered a public data-set for the simulation of predictive maintenance scenarios. We used a Convolution Neural Network (CNN) architecture, which provided competitive performance over the state-of-the-art approaches, while being approximately four-times and two-times faster than the uncompressed model inference on the Central Processing Unit (CPU) and Graphical Processing Unit, respectively. These results highlight the capabilities of distributed large-scale edge computing over industrial scenarios.DF – Departamento de FísicaLIBPhys-UNLRUNResende, CarlosFolgado, DuarteOliveira, JoãoFranco, BernardoMoreira, WaldirOliveira-Jr, AntonioCavaleiro, ArmandoCarvalho, Ricardo2022-12-02T22:14:37Z2021-07-082021-07-08T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article23application/pdfhttp://hdl.handle.net/10362/145960eng1424-8220PURE: 45613304https://doi.org/10.3390/s21144676info: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:RCAAP2024-03-11T05:26:50Zoai:run.unl.pt:10362/145960Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:52:22.274195Repositório 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 Tip4.0
Industrial internet of things platform for predictive maintenance
title Tip4.0
spellingShingle Tip4.0
Resende, Carlos
Artificial intelligence
Edge computing
Industry 4.0
Internet of things
Predictive maintenance
Analytical Chemistry
Information Systems
Atomic and Molecular Physics, and Optics
Biochemistry
Instrumentation
Electrical and Electronic Engineering
SDG 9 - Industry, Innovation, and Infrastructure
SDG 16 - Peace, Justice and Strong Institutions
title_short Tip4.0
title_full Tip4.0
title_fullStr Tip4.0
title_full_unstemmed Tip4.0
title_sort Tip4.0
author Resende, Carlos
author_facet Resende, Carlos
Folgado, Duarte
Oliveira, João
Franco, Bernardo
Moreira, Waldir
Oliveira-Jr, Antonio
Cavaleiro, Armando
Carvalho, Ricardo
author_role author
author2 Folgado, Duarte
Oliveira, João
Franco, Bernardo
Moreira, Waldir
Oliveira-Jr, Antonio
Cavaleiro, Armando
Carvalho, Ricardo
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv DF – Departamento de Física
LIBPhys-UNL
RUN
dc.contributor.author.fl_str_mv Resende, Carlos
Folgado, Duarte
Oliveira, João
Franco, Bernardo
Moreira, Waldir
Oliveira-Jr, Antonio
Cavaleiro, Armando
Carvalho, Ricardo
dc.subject.por.fl_str_mv Artificial intelligence
Edge computing
Industry 4.0
Internet of things
Predictive maintenance
Analytical Chemistry
Information Systems
Atomic and Molecular Physics, and Optics
Biochemistry
Instrumentation
Electrical and Electronic Engineering
SDG 9 - Industry, Innovation, and Infrastructure
SDG 16 - Peace, Justice and Strong Institutions
topic Artificial intelligence
Edge computing
Industry 4.0
Internet of things
Predictive maintenance
Analytical Chemistry
Information Systems
Atomic and Molecular Physics, and Optics
Biochemistry
Instrumentation
Electrical and Electronic Engineering
SDG 9 - Industry, Innovation, and Infrastructure
SDG 16 - Peace, Justice and Strong Institutions
description POCI-01-0247-FEDER-038436
publishDate 2021
dc.date.none.fl_str_mv 2021-07-08
2021-07-08T00:00:00Z
2022-12-02T22:14:37Z
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/10362/145960
url http://hdl.handle.net/10362/145960
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1424-8220
PURE: 45613304
https://doi.org/10.3390/s21144676
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 23
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
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