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dc.rights.licenseVisos teisės saugomos / All rights reserveden_US
dc.contributor.authorLiulys, Karolis
dc.date.accessioned2025-12-11T13:05:50Z
dc.date.available2025-12-11T13:05:50Z
dc.date.issued2019
dc.identifier.isbn9781728125008en_US
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/159523
dc.description.abstractIndustrial organizations worldwide cannot ignore Industry 4.0 and its impact to their businesses. The biggest struggle is to find the way how to adopt all the possibilities for each plants unique use cases. In those situations where it is hard to find unified solutions internet is playing major part. Inseparable part of Industry 4.0 is Internet of Things (IoT) paradigm, where it is possible to connect all devices into united system. While robust Distributed Control Systems (DCS) are preferred for their safety, Industrial IoT (IIoT) allows next level prospects: big data performance analyzation, control patterns identification and predictive preventative maintenance by using machine learning algorithms. The study shows how implementing open-source software enables engineers to develop predictive maintenance applications with basic programming knowledge. These type of applications can be widely used in industrial field to inform about possible equipment malfunction helping reduce possible damages.en_US
dc.format.extent4 p.en_US
dc.format.mediumTekstas / Texten_US
dc.language.isoenen_US
dc.relation.urihttps://etalpykla.vilniustech.lt/handle/123456789/159393en_US
dc.source.urihttps://ieeexplore.ieee.org/document/8732146en_US
dc.subjectIndustry 4.0en_US
dc.subjectpreventive maintenanceen_US
dc.subjectmachine learningen_US
dc.titleMachine Learning Application in Predictive Maintenanceen_US
dc.typeKonferencijos publikacija / Conference paperen_US
dcterms.accrualMethodRankinis pateikimas / Manual submissionen_US
dcterms.issued2019-06-06
dcterms.references10en_US
dc.description.versionTaip / Yesen_US
dc.contributor.institutionVilniaus Gedimino technikos universitetasen_US
dc.contributor.institutionVilnius Gediminas Technical Universityen_US
dc.contributor.departmentElektros inžinerijos katedra / Department of Electrical Engineeringen_US
dcterms.sourcetitle2019 Open Conference of Electrical, Electronic and Information Sciences (eStream), April 25, 2019, Vilnius, Lithuaniaen_US
dc.identifier.eisbn9781728124995en_US
dc.publisher.nameIEEEen_US
dc.publisher.countryUnited States of Americaen_US
dc.publisher.cityNew Yorken_US
dc.identifier.doihttps://doi.org/10.1109/eStream.2019.8732146en_US


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