Rodyti trumpą aprašą

dc.contributor.authorAsad, Bilal
dc.contributor.authorRaja, Hadi Ashraf
dc.contributor.authorVainmann, Toomas
dc.contributor.authorKallaste, Ants
dc.contributor.authorPomarnacki, Raimondas
dc.contributor.authorHyunh, Van Khang
dc.date.accessioned2023-09-18T16:39:25Z
dc.date.available2023-09-18T16:39:25Z
dc.date.issued2023
dc.identifier.issn2079-9292
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/115484
dc.description.abstractAn algorithm to improve the resolution of the frequency spectrum by detecting the number of complete cycles, removing any fractional components of the signal, signal discontinuities, and interpolating the signal for fault diagnostics of electrical machines using low-power data acquisition cards is proposed in this paper. Smart sensor-based low-power data acquisition and processing devices such as Arduino cards are becoming common due to the growing trend of the Internet of Things (IoT), cloud computation, and other Industry 4.0 standards. For predictive maintenance, the fault representing frequencies at the incipient stage are very difficult to detect due to their small amplitude and the leakage of powerful frequency components into other parts of the spectrum. For this purpose, offline advanced signal processing techniques are used that cannot be performed in small signal processing devices due to the required computational time, complexity, and memory. Hence, in this paper, an algorithm is proposed that can improve the spectrum resolution without complex advanced signal processing techniques and is suitable for low-power signal processing devices. The results both from the simulation and practical environment are presented.eng
dc.formatPDF
dc.format.extentp. 1-15
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyScience Citation Index Expanded (Web of Science)
dc.relation.isreferencedbyScopus
dc.source.urihttps://www.mdpi.com/2079-9292/12/7/1746/htm
dc.titleA current spectrum-based algorithm for fault detection of electrical machines using low-power data acquisition devices
dc.typeStraipsnis Web of Science DB / Article in Web of Science DB
dcterms.accessRightsThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
dcterms.licenseCreative Commons – Attribution – 4.0 International
dcterms.references76
dc.type.pubtypeS1 - Straipsnis Web of Science DB / Web of Science DB article
dc.contributor.institutionThe Islamia University of Bahawalpur Tallinn University of Technology
dc.contributor.institutionTallinn University of Technology
dc.contributor.institutionVilniaus Gedimino technikos universitetas
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.studydirectionE09 - Elektronikos inžinerija / Electronic engineering
dc.subject.vgtuprioritizedfieldsIK0202 - Išmaniosios signalų apdorojimo ir ryšių technologijos / Smart Signal Processing and Telecommunication Technologies
dc.subject.ltspecializationsL106 - Transportas, logistika ir informacinės ir ryšių technologijos (IRT) / Transport, logistic and information and communication technologies
dc.subject.enelectrical machine
dc.subject.enmachine learning
dc.subject.endata acquisition
dc.subject.enFEM
dc.subject.ensignal processing
dc.subject.enArduino
dc.subject.enartificial intelligence
dcterms.sourcetitleElectronics: Advanced Fault Detection, Diagnosis and Prognosis in a Context of Renewable Power Generation
dc.description.issueiss. 7
dc.description.volumevol. 12
dc.publisher.nameMDPI
dc.publisher.cityBasel
dc.identifier.doi000971825900001
dc.identifier.doi10.3390/electronics12071746
dc.identifier.elaba161392611


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