The Performance Evaluation of Machine Learning based Techniques via Stator Current and Stray Flux for Broken Bar Fault in Induction Motors

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dc.authoridGurusamy, Vigneshwaran/0000-0002-9366-1777
dc.authoridGoktas, Taner/0000-0002-8218-3239
dc.authorwosidGurusamy, Vigneshwaran/ABU-7865-2022
dc.authorwosidGoktas, Taner/ABG-9388-2020
dc.contributor.authorYounas, M. B.
dc.contributor.authorUllah, N.
dc.contributor.authorGoktas, Taner
dc.contributor.authorArkan, Muslum
dc.contributor.authorGurusamy, V.
dc.date.accessioned2024-08-04T20:51:36Z
dc.date.available2024-08-04T20:51:36Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.description13th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED) -- AUG 22-25, 2021 -- Dallas, TXen_US
dc.description.abstractIn this paper, the machine learning based techniques are evaluated using stator current and stray flux for broken bar fault in induction motors (IMs). The feature extraction is achieved from Discrete Wavelet Transform (DWT) for both healthy and faulty operations. In order to analyze the performance of different classifier, six fundamental classifications with 23 sub-classifiers are used via a toolbox. It has been observed that 18 out of 23 classifiers have shown great performance (100% accuracy) and two more classifier results at accuracy of greater than 90% for stray flux. Both simulation and experimental results show that stray flux provides better diagnostics results than stator current using different machine learning based classification algorithms in IMs.en_US
dc.description.sponsorshipIEEEen_US
dc.identifier.doi10.1109/SDEMPED51010.2021.9605516
dc.identifier.endpage185en_US
dc.identifier.isbn978-1-7281-9297-0
dc.identifier.scopus2-s2.0-85123314164en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage180en_US
dc.identifier.urihttps://doi.org/10.1109/SDEMPED51010.2021.9605516
dc.identifier.urihttps://hdl.handle.net/11616/100435
dc.identifier.wosWOS:000904997200028en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2021 Ieee 13th International Symposium on Diagnostics For Electrical Machines, Power Electronics and Drives (Sdemped)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBroken rotor bar faulten_US
dc.subjectdiscrete wavelet transformen_US
dc.subjectfault diagnosisen_US
dc.subjectinduction motoren_US
dc.subjectmachine learning techniquesen_US
dc.titleThe Performance Evaluation of Machine Learning based Techniques via Stator Current and Stray Flux for Broken Bar Fault in Induction Motorsen_US
dc.typeConference Objecten_US

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