The detection of rotor faults using artificial neural network

Basit Öğe Kaydı
dc.contributor.authorArabaci, Hayri
dc.contributor.authorBilgin, Osman
dc.date.accessioned2020-03-26T17:04:30Z
dc.date.available2020-03-26T17:04:30Z
dc.date.issued2006
dc.departmentSelçuk Üniversitesien_US
dc.descriptionIEEE 14th Signal Processing and Communications Applications -- APR 16-19, 2006 -- Antalya, TURKEYen_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.en_US
dc.description.sponsorshipIEEEen_US
dc.identifier.endpage+en_US
dc.identifier.isbn978-1-4244-0238-0
dc.identifier.startpage265en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/20715
dc.identifier.wosWOS:000245347800068en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isotren_US
dc.publisherIEEEen_US
dc.relation.ispartof2006 IEEE 14TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS, VOLS 1 AND 2en_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 networken_US
dc.typeConference Objecten_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu