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Artificial intelligence in monitoring and diagnostics of electrical energy conversion systems

dc.contributor.authorVaimann, Toomas
dc.contributor.authorRassolkin, Anton
dc.contributor.authorKallaste, Ants
dc.contributor.authorPomarnacki, Raimondas
dc.contributor.authorBelahcen, Anouar
dc.contributor.authorHyunh, Van Khang
dc.date.accessioned2023-09-18T20:34:20Z
dc.date.available2023-09-18T20:34:20Z
dc.date.issued2020
dc.identifier.other(SCOPUS_ID)85084418022
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/150953
dc.description.abstractDiagnostics and prognostics of electrical energy conversion systems are moving forward with the rapid development of IT and artificial intelligence possibilities. This also broadens the horizons for classical and advanced condition and operation monitoring techniques, resulting in more accurate fault detection, degradation prognosis and calculation of remaining life of energy conversion systems, utilized in every aspect and field of industry today. This paper gives an overview of the necessity for condition monitoring and diagnostics of the mentioned systems, explaining the classical and advanced techniques for diagnostics. Methodology to diagnose and prognose the energy conversion units, where classical maintenance techniques are not sufficient in the economic, environmental and safety reasons is proposed. An extensive state of art in the field of diagnostics, regarding the aforementioned problems and techniques is provided.eng
dc.formatPDF
dc.format.extentp. 1-2
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyScopus
dc.relation.isreferencedbyIEEE Xplore
dc.source.urihttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9069566
dc.subjectH600 - Elektronikos ir elektros inžinerija / Electronic and electrical engineering
dc.titleArtificial intelligence in monitoring and diagnostics of electrical energy conversion systems
dc.typeStraipsnis konferencijos darbų leidinyje Scopus DB / Paper in conference publication in Scopus DB
dcterms.references40
dc.type.pubtypeP1b - Straipsnis konferencijos darbų leidinyje Scopus DB / Article in conference proceedings Scopus DB
dc.contributor.institutionTallinn University of Technology
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.institutionAalto University
dc.contributor.institutionUniversity of Agder
dc.contributor.facultyElektronikos fakultetas / Faculty of Electronics
dc.subject.researchfieldT 001 - Elektros ir elektronikos inžinerija / Electrical and electronic engineering
dc.subject.vgtuprioritizedfieldsIK0303 - Dirbtinio intelekto ir sprendimų priėmimo sistemos / Artificial intelligence and decision support systems
dc.subject.ltspecializationsL106 - Transportas, logistika ir informacinės ir ryšių technologijos (IRT) / Transport, logistic and information and communication technologies
dc.subject.enartificial intelligence
dc.subject.enenergy conversion
dc.subject.enfault detection
dc.subject.enmachine learning
dcterms.sourcetitle2020 27th International Workshop on Electric Drives: MPEI Department of Electric Drives 90th Anniversary (IWED), Moscow, Russia, January 27–30, 2020: proceedings
dc.publisher.nameIEEE
dc.publisher.cityPiscataway, NJ
dc.identifier.doi2-s2.0-85084418022
dc.identifier.doi85084418022
dc.identifier.doi0
dc.identifier.doi10.1109/IWED48848.2020.9069566
dc.identifier.elaba73919376


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