A combined HT and ANN based early broken bar fault diagnosis approach for IFOC fed induction motor drive

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dc.authoridRAMU, SENTHIL KUMAR/0000-0001-9175-3814
dc.authorids, ishwarya/0000-0002-7993-7589
dc.authoridAlhamrouni, Ibrahim/0000-0003-1035-3005
dc.authoridIrudayaraj, Gerald Christopher Raj/0000-0002-8529-7303
dc.contributor.authorKumar, R. Senthil
dc.contributor.authorRaj, I. Gerald Christopher
dc.contributor.authorAlhamrouni, Ibrahim
dc.contributor.authorSaravanan, S.
dc.contributor.authorPrabaharan, Natarajan
dc.contributor.authorIshwarya, S.
dc.contributor.authorGokdag, Mustafa
dc.date.accessioned2024-09-29T15:54:54Z
dc.date.available2024-09-29T15:54:54Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractIn recent years, fault diagnosis in the Induction Motor Drive (IMD) has been a popular and important field in the motor-drive research area. The development of control circuits for induction motors has prompted the attention of both researchers and industrialists. This paper proposes a broken bar fault diagnosis using Hilbert Transform (HT) and Artificial Neural Networks (ANN), with the drive regulated through the Indirect Field Orientation Control (IFOC). The HT obtains the spectrum of stator current, which is utilized to identify the Broken Rotor Bar (BRB) failure. The magnitude and side-band frequency of the drive are extracted using the Fast Fourier Transform (FFT), and these parameters are fed into the ANN inputs. The fault severity is computed by the ratio of mean side-band frequency amplitude to the main frequency amplitude for finding the impact of failure in the drive. ANN is used to diagnose failure with high accuracy. The tested and training results are used to attain the minimum Mean Square Errors (MSEs). The IFOC is involved in this proposed system to ensure high performance under the variable speed drives. The proposed scheme is validated in both MATLAB/Simulink and experimental platforms.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).en_US
dc.description.sponsorshipUniversiti Kuala Lumpur [UniKL/CoRI/UER20003]en_US
dc.description.sponsorshipAuthors would like to acknowledge the financial support received from Universiti Kuala Lumpur under grant number UniKL/CoRI/UER20003.en_US
dc.identifier.doi10.1016/j.aej.2022.12.010
dc.identifier.endpage30en_US
dc.identifier.issn1110-0168
dc.identifier.issn2090-2670
dc.identifier.scopus2-s2.0-85144255624en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage15en_US
dc.identifier.urihttps://doi.org/10.1016/j.aej.2022.12.010
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4352
dc.identifier.volume66en_US
dc.identifier.wosWOS:000923207600001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofAlexandria Engineering Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial neural networksen_US
dc.subjectFault diagnosisen_US
dc.subjectHilbert transformen_US
dc.subjectSeverity factoren_US
dc.subjectIndirect field oriented conen_US
dc.subjecttrolen_US
dc.subjectFast fourier transformen_US
dc.titleA combined HT and ANN based early broken bar fault diagnosis approach for IFOC fed induction motor driveen_US
dc.typeArticleen_US

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