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Monitoring electrical and operational parameters of a stamping machine for failure prediction

dc.contributor.authorPecora, Pedro
dc.contributor.authorGarcia, Fernando Feijoo
dc.contributor.authorMelo, Victória
dc.contributor.authorLeitão, Paulo
dc.contributor.authorPellegri, Umberto
dc.date.accessioned2023-03-21T11:44:10Z
dc.date.available2023-03-21T11:44:10Z
dc.date.issued2022
dc.description.abstractGiven the industrial environment, the production efficiency is the ultimate goal to achieve a high standard. Any deviation from the standard can be costly, e.g., a malfunction of a machine in an assembly line tends to have a major setback in the overall factory efficiency. The data value brought by the advent of Industry 4.0 re-shaped the way that processes and machines are managed, being possible to analyse the collected data in real-time to identify and prevent machine malfunctions. In this work, a monitoring and prediction system was developed on a cold stamping machine focusing on its electrical and operational parameters, based on the Digital Twin approach. The proposed system ranges from data collection to visualization, condition monitoring and prediction. The collected data is visualized via dashboards created to provide insights of the machine status, alongside with visual alerts related to the early detection of trends and outliers in the machine’s operation. The analysis of the current intensity is carried out aiming to predict failures and warn the maintenance team about possible future disturbances in the machine condition.pt_PT
dc.description.sponsorshipSupported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope UIDB/05757/2020.
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationPecora, Pedro; Garcia, Fernando Feijoo; Melo, Victória; Leitão, Paulo; Pellegri, Umberto (2022). Monitoring electrical and operational parameters of a stamping machine for failure prediction. In 2nd International Conference on Optimization, Learning Algorithms and Applications, OL2A 2022. Póvoa de Varzim, Portugalpt_PT
dc.identifier.doi10.1007/978-3-031-23236-7_50pt_PT
dc.identifier.isbn978-3-031-23236-7
dc.identifier.urihttp://hdl.handle.net/10198/27898
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer, Champt_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectCondition monitoringpt_PT
dc.subjectDigital twinpt_PT
dc.subjectIoTpt_PT
dc.titleMonitoring electrical and operational parameters of a stamping machine for failure predictionpt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.citation.conferencePlacePóvoa de Varzim, Portugalpt_PT
oaire.citation.endPage743pt_PT
oaire.citation.startPage729pt_PT
oaire.citation.title2nd International Conference on Optimization, Learning Algorithms and Applications, OL2A 2022pt_PT
oaire.citation.volume1754pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameMelo
person.familyNameLeitão
person.givenNameVictória
person.givenNamePaulo
person.identifierhttps://scholar.google.com/citations?user=2JvgMH4AAAAJ&hl=pt-PT
person.identifierA-8390-2011
person.identifier.ciencia-id6214-95F1-0036
person.identifier.ciencia-id8316-8F13-DA71
person.identifier.orcid0000-0003-0025-8055
person.identifier.orcid0000-0002-2151-7944
person.identifier.scopus-author-id57216635456
person.identifier.scopus-author-id35584388900
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublicationf09c045b-fb10-4bb8-8c45-168748aa38a5
relation.isAuthorOfPublication68d9eb25-ad4f-439b-aeb2-35e8708644cc
relation.isAuthorOfPublication.latestForDiscoveryf09c045b-fb10-4bb8-8c45-168748aa38a5
relation.isProjectOfPublication6e01ddc8-6a82-4131-bca6-84789fa234bd
relation.isProjectOfPublication.latestForDiscovery6e01ddc8-6a82-4131-bca6-84789fa234bd

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