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Artificial intelligence data-driven petri nets approach for virtualizing digital twins

dc.contributor.authorOliveira Júnior, Alexandre de
dc.contributor.authorCalvo-Rolle, Jose Luis
dc.contributor.authorLeitão, Paulo
dc.date.accessioned2024-01-09T10:11:48Z
dc.date.available2024-01-09T10:11:48Z
dc.date.issued2023
dc.description.abstractVirtualization is one key design principle in Industry 4.0, with the modeling and simulation of the physical assets playing crucial roles in the Digital Twin context. Different approaches can be used to implement the virtual asset models, ranging from simple equations to complex mathematical models. Petri nets formalism is a suitable approach to model and simulate the physical asset operation in the Digital context, particularly those that are event-driven, taking advantage of its inherent robust mathematical foundation. Having this in mind, this paper proposes a Petri nets approach, which considers Artificial Intelligent data-driven analytics associated to timed transitions to support the execution of what-if simulation aiming the monitoring, diagnosis, prediction, and optimization. The proposed approach was tested in an experimental punching machine, allowing the early identification the performance degradation in the Digital Twin and the selection of actions to be implemented in the physical asset to improve its operation.pt_PT
dc.description.sponsorshipThis work has been supported by the Foundation for Science and Technology (FCT, Portugal) through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationOliveira Júnior, Alexandre de; Calvo-Rolle, Jose Luis; Leitão, Paulo (2023). Artificial intelligence data-driven petri nets approach for virtualizing digital twins. In 2023 IEEE International Conference on Industrial Technology (ICIT). 04-06 April 2023, Orlando. ISSN 2643-2978. p. 1-6pt_PT
dc.identifier.doi10.1109/ICIT58465.2023.10143087pt_PT
dc.identifier.issn2643-2978
dc.identifier.urihttp://hdl.handle.net/10198/29146
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEEpt_PT
dc.relationLA/P/0007/2021pt_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectDigital twinpt_PT
dc.subjectPetri netspt_PT
dc.subjectModelingpt_PT
dc.subjectSimulationpt_PT
dc.titleArtificial intelligence data-driven petri nets approach for virtualizing digital twinspt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT
oaire.citation.endPage6pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.title2023 IEEE International Conference on Industrial Technology (ICIT)pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameOliveira Júnior
person.familyNameLeitão
person.givenNameAlexandre de
person.givenNamePaulo
person.identifierhttps://scholar.google.com/citations?user=WurUUUUAAAAJ&hl=pt-PT&oi=ao
person.identifierA-8390-2011
person.identifier.ciencia-id7311-463F-3FA3
person.identifier.ciencia-id8316-8F13-DA71
person.identifier.orcid0000-0002-7009-3965
person.identifier.orcid0000-0002-2151-7944
person.identifier.scopus-author-id57426745400
person.identifier.scopus-author-id35584388900
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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relation.isAuthorOfPublication68d9eb25-ad4f-439b-aeb2-35e8708644cc
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