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A cost-effective solution for predictive maintenance in industrial pumping systems

2024-12-08Documento de conferência Acesso restrito
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datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
datacite.subject.sdg12:Produção e Consumo Sustentáveis
datacite.subject.sdg07:Energias Renováveis e Acessíveis
dc.contributor.authorBrito, Sergio
dc.contributor.authorAzinheira, Gonçalo
dc.contributor.authorSemião, Jorge
dc.contributor.authorSousa, Nelson
dc.date.accessioned2026-05-25T14:46:56Z
dc.date.available2026-05-25T14:46:56Z
dc.date.issued2024-12-08
dc.description.abstractThis paper presents a cost-effective, Internet of Things (IoT)-based solution for predictive maintenance (PdM) in industrial pumping systems. The proposed system integrates custom-built hardware with machine learning (ML) algorithms to monitor and detect anomalies in real-time. The innovation of the system lies in its non-intrusive design, ease of installation, and adaptability to a variety of industrial environments, providing a practical, low-cost alternative to traditional PdM solutions. Detailed discussion is provided on the hardware component selection, which prioritizes affordability without sacrificing performance, as well as the machine learning strategies used for anomaly detection. Preliminary results from laboratory and field testing demonstrate the system’s potential for reducing downtime and maintenance costs, with a focus on extending the application to broader industrial contexts.eng
dc.identifier.doi10.1109/oncon62778.2024.10931406
dc.identifier.urihttp://hdl.handle.net/10400.1/29029
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE
dc.relation.ispartof2024 IEEE 3rd Industrial Electronics Society Annual On-Line Conference (ONCON)
dc.rights.uriN/A
dc.subjectPredictive
dc.subjectMaintenance
dc.subjectInternet of things
dc.subjectMachine learning
dc.subjectIndustrial monitoring
dc.subjectAnomaly detection
dc.titleA cost-effective solution for predictive maintenance in industrial pumping systemseng
dc.typeconference object
dspace.entity.typePublication
oaire.citation.title2024 IEEE 3rd Industrial Electronics Society Annual On-Line Conference, ONCON 2024
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameBrito
person.familyNameAzinheira
person.familyNameSemião
person.familyNameSousa
person.givenNameSergio
person.givenNameGonçalo
person.givenNameJorge
person.givenNameNelson
person.identifier3016271
person.identifierR-001-F67
person.identifier.ciencia-idB616-9C41-C169
person.identifier.ciencia-id8C16-E46C-4C86
person.identifier.orcid0009-0008-7287-0431
person.identifier.orcid0000-0002-3139-2081
person.identifier.orcid0000-0002-7667-7910
person.identifier.orcid0000-0001-5205-8608
person.identifier.ridL-6700-2015
person.identifier.scopus-author-id15924042200
person.identifier.scopus-author-id57198012719
relation.isAuthorOfPublication458c5d10-4804-434b-a2a7-8d1d3ce16034
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relation.isAuthorOfPublication.latestForDiscovery458c5d10-4804-434b-a2a7-8d1d3ce16034

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