Real-time condition monitoring and fault diagnosis in switched reluctance motors with Kohonen neural network

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dc.contributor.authorUysal, Ali
dc.contributor.authorBayir, Raif
dc.date.accessioned2024-09-29T16:06:27Z
dc.date.available2024-09-29T16:06:27Z
dc.date.issued2013
dc.departmentKarabük Üniversitesien_US
dc.description.abstractThe faults in switched reluctance motors (SRMs) were detected and diagnosed in real time with the Kohonen neural network. When a fault happens, both financial losses and undesired situations may occur. For these reasons, it is important to detect the incipient faults of SRMs and to diagnose which faults have occurred. In this study, a test rig was realized to determine the healthy and faulty conditions of SRMs. A data set for the Kohonen neural network was created with implemented measurements. A graphical user interface (GUI) was created in Matlab to test the performance of the Kohonen artificial neural network in real time. The data of the SRM was transferred to this software with a data acquisition card. The condition of the motor was monitored by marking the data measured in real time on the weight position graph of the Kohonen neural network. This test rig is capable of real-time monitoring of the condition of SRMs, which are used with intermittent or continuous operation, and is capable of detecting and diagnosing the faults that may occur in the motor. The Kohonen neural network used for detection and diagnosis of faults of the SRM in real time with Matlab GUI was embedded in an STM32 processor. A prototype with the STM32 processor was developed to detect and diagnose the faults of SRMs independent of computers.en_US
dc.description.sponsorshipKarabuk University BAP Unit, Turkey [KBU-BAP-C-11-D-003]en_US
dc.description.sponsorshipProject (No. KBU-BAP-C-11-D-003) supported by the Karabuk University BAP Unit, Turkeyen_US
dc.identifier.doi10.1631/jzus.C1300085
dc.identifier.endpage952en_US
dc.identifier.issn1869-1951
dc.identifier.issn1869-196X
dc.identifier.issue12en_US
dc.identifier.scopus2-s2.0-84892741480en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage941en_US
dc.identifier.urihttps://doi.org/10.1631/jzus.C1300085
dc.identifier.urihttps://hdl.handle.net/20.500.14619/6830
dc.identifier.volume14en_US
dc.identifier.wosWOS:000328364300005en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherZhejiang Univen_US
dc.relation.ispartofJournal of Zhejiang University-Science C-Computers & Electronicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSwitched reluctance motoren_US
dc.subjectKohonen neural networken_US
dc.subjectReal-time condition monitoringen_US
dc.subjectFault detection and diagnosisen_US
dc.titleReal-time condition monitoring and fault diagnosis in switched reluctance motors with Kohonen neural networken_US
dc.typeArticleen_US

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