Broken magnets fault detection in PMSM using a convolutional neural network and SVM

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dc.authorid0000-0001-8461-8702en_US
dc.contributor.authorBenkaihoul, Said
dc.contributor.authorMazouz, Lakhdar Djelloul
dc.contributor.authorTayeb, Naas Toufik
dc.contributor.authorÖzüpak, Yıldırım
dc.contributor.authorMohammedi, Ridha Djamel
dc.date.accessioned2024-08-19T11:32:15Z
dc.date.available2024-08-19T11:32:15Z
dc.date.issued2024en_US
dc.departmentDicle Üniversitesi, Silvan Meslek Yüksek Okulu, Elektrik ve Enerji Bölümüen_US
dc.description.abstractThe Permanent Magnet Synchronous Motor (PMSM) stands as a pivotal component in various applications, yet it remains susceptible to an array of faults within both its rotor and stator, there arises an imperative to swiftly and intelligently address these issues. In this study, a novel approach was undertaken wherein a PMSM design was conceptualized within the Ansys Maxwell program, followed by the deliberate introduction of a fault at the rotor's magnetic level. Specifically, three distinct fault scenarios were delineated based on the number of broken magnets (BM), namely 2, 3, and 4, localized within specific rotor areas. Notably, the magnetic flux density was selected as the focal parameter for this investigation. To effectively detect and diagnose faults stemming from BM, an innovative Convolutional Neural Network (CNN) architecture was devised. Leveraging images of the PMSM design captured during operational phases at various time intervals, the CNN exhibited remarkable efficacy in discerning and categorizing fault instances. Upon analysis of the derived outcomes, it becomes evident that the CNN exhibited unparalleled accuracy in fault detection, achieving a remarkable 100% success rate when juxtaposed with alternative methodologies such as Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), which yielded accuracy rates of 97%.en_US
dc.identifier.citationBenkaihoul, S., Mazouz, L., Naas, T. T., Özüpak, Y. ve Mohammedi, R. R. (2024). Broken magnets fault detection in PMSM using a convolutional neural network and SVM. Journal of Engineering and Technology for Industrial Applications, 10(48), 55-62.en_US
dc.identifier.endpage62en_US
dc.identifier.issn2447-0228
dc.identifier.issue48en_US
dc.identifier.scopus2-s2.0-85200650319
dc.identifier.scopusqualityQ4
dc.identifier.startpage55en_US
dc.identifier.urihttps://itegam-jetia.org/journal/index.php/jetia/article/view/1185/732
dc.identifier.urihttps://hdl.handle.net/11468/28763
dc.identifier.volume10en_US
dc.indekslendigikaynakScopus
dc.institutionauthorÖzüpak, Yıldırım
dc.language.isoenen_US
dc.publisherGalileo Institute of Technology and Education of the Amazon (ITEGAM)en_US
dc.relation.ispartofJournal of Engineering and Technology for Industrial Applications
dc.relation.isversionof10.5935/jetia.v10i48.1185en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBroken magnetsen_US
dc.subjectConvolutional neural networken_US
dc.subjectFault detectionen_US
dc.subjectMagnetic flux densityen_US
dc.subjectPMSMen_US
dc.titleBroken magnets fault detection in PMSM using a convolutional neural network and SVMen_US
dc.titleBroken magnets fault detection in PMSM using a convolutional neural network and SVM
dc.typeArticleen_US

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