The detection of rotor faults using artificial neural network [Yapay si?ni?r a?lari kullanarak rotor arizalarinin teşhi?si?]

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dc.contributor.authorArabaci H.
dc.contributor.authorBilgin O.
dc.date.accessioned2020-03-26T17:04:59Z
dc.date.available2020-03-26T17:04:59Z
dc.date.issued2006
dc.departmentSelçuk Üniversitesien_US
dc.description2006 IEEE 14th Signal Processing and Communications Applications -- 17 April 2006 through 19 April 2006 -- Antalya -- 69461en_US
dc.description.abstractThe detection of broken rotor bars in tree-phase squirrel cage induction motors by means of current signature analysis is presented. In order to diagnose faults, a Neural Network approach is used. At first the data of different rotor faults are achieved. The effects of different rotor faults on current spectrum, in comparison with other fault conditions, are investigated via calculating Power Spectrum Density (PSD). Training the Neural Network discern between "healthy" and "faulty" motor conditions by using experimental data in case of healthy and faulted motor. The test results clearly illustrate that the stator current signature can be used to diagnose faults of squirrel cage rotor. © 2006 IEEE.en_US
dc.identifier.doi10.1109/SIU.2006.1659690en_US
dc.identifier.isbn1424402395; 9781424402397
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://dx.doi.org/10.1109/SIU.2006.1659690
dc.identifier.urihttps://hdl.handle.net/20.500.12395/20816
dc.identifier.volume2006en_US
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.relation.ispartof2006 IEEE 14th Signal Processing and Communications Applications Conferenceen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.titleThe detection of rotor faults using artificial neural network [Yapay si?ni?r a?lari kullanarak rotor arizalarinin teşhi?si?]en_US
dc.typeConference Objecten_US

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