dc.contributor.author | Liulys, Karolis | |
dc.date.accessioned | 2023-09-18T19:03:32Z | |
dc.date.available | 2023-09-18T19:03:32Z | |
dc.date.issued | 2019 | |
dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/134991 | |
dc.description.abstract | Industrial 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. | eng |
dc.format | PDF | |
dc.format.extent | p. 1-4 | |
dc.format.medium | tekstas / txt | |
dc.language.iso | eng | |
dc.relation.isreferencedby | Conference Proceedings Citation Index - Science (Web of Science) | |
dc.relation.isreferencedby | INSPEC | |
dc.relation.isreferencedby | IEEE Xplore | |
dc.relation.isreferencedby | Scopus | |
dc.title | Machine learning application in predictive maintenance | |
dc.type | Straipsnis konferencijos darbų leidinyje Web of Science DB / Paper in conference publication in Web of Science DB | |
dcterms.references | 10 | |
dc.type.pubtype | P1a - Straipsnis konferencijos darbų leidinyje Web of Science DB / Article in conference proceedings Web of Science DB | |
dc.contributor.institution | Vilniaus Gedimino technikos universitetas | |
dc.contributor.faculty | Elektronikos fakultetas / Faculty of Electronics | |
dc.subject.researchfield | T 001 - Elektros ir elektronikos inžinerija / Electrical and electronic engineering | |
dc.subject.vgtuprioritizedfields | MC0505 - Inovatyvios elektroninės sistemos / Innovative Electronic Systems | |
dc.subject.ltspecializations | L104 - Nauji gamybos procesai, medžiagos ir technologijos / New production processes, materials and technologies | |
dc.subject.en | Industry 4.0 | |
dc.subject.en | IoT | |
dc.subject.en | preventive maintenance | |
dc.subject.en | machine learning | |
dcterms.sourcetitle | 2019 Open Conference of Electrical, Electronic and Information Sciences (eStream), 25 April 2019, Vilnius, Lithuania : proceedings of the conference / organized by: Vilnius Gediminas Technical University | |
dc.publisher.name | IEEE | |
dc.publisher.city | New York | |
dc.identifier.doi | 2-s2.0-85068386773 | |
dc.identifier.doi | 000492889800003 | |
dc.identifier.doi | 10.1109/eStream.2019.8732146 | |
dc.identifier.elaba | 39786905 | |