Neural Network Classification and Diagnosis of Broken Rotor Bar Faults by Means of Short Time Fourier Transform

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dc.contributor.authorArabaci, Hayri
dc.contributor.authorBilgin, Osman
dc.date.accessioned2020-03-26T17:39:19Z
dc.date.available2020-03-26T17:39:19Z
dc.date.issued2009
dc.departmentSelçuk Üniversitesien_US
dc.descriptionInternational Multi-Conference of Engineers and Computer Scientists -- MAR 18-20, 2009 -- Kowloon, PEOPLES R CHINAen_US
dc.description.abstractIn this paper an experimental study of classification and diagnosis of different number of broken rotor bars and broken end-ring in the three-phase squirrel cage induction motors is presented. Six different faulted rotors are investigated. These faults are one, two, three broken bars, broken end-ring, a bar with high resistance and healthy rotor. The base structure of the study consist of current signal analysis (CSA), feature extraction, Artificial Neural Network (ANN) and diagnosis algorithm. The motor current signal is used for obtaining of effects of broken bars and end-ring in the rotor. To get sight of the effects the current signal that is in the time domain is transformed time-frequency domain via Short Time Fourier Transform (STFT). And the spectrums are averaged and normalized on the time axis. The rotor cage faults are classified with ANN by using these spectrums. And result matrixes of ANN are considered improved decision structure. Thus the faulted rotors are diagnosed at 100% accuracy and classified 98,33% accuracy.en_US
dc.description.sponsorshipInt Assoc Engineers, IAENG, Soc Artificial Intelligence, IAENG, Soc Bioinformat, IAENG, Soc Comp Sci, IAENG, Soc Data Min, IAENG, Soc Elect Engn, IAENG, Soc Imaging Engn, IAENG, Soc Ind Engn, IAENG, Soc Informat Syst Engn, IAENG, Soc Internet Comp & Web Serv, IAENG, Soc Mech Engn, IAENG, Soc Operat Res, IAENG, Soc Sci Comp, IAENG, Soc Software Engn, IAENG, Soc Wireless Networksen_US
dc.identifier.endpage223en_US
dc.identifier.isbn978-988-17012-2-0
dc.identifier.issn2078-0958en_US
dc.identifier.startpage219en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/23692
dc.identifier.wosWOS:000266097200041en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherINT ASSOC ENGINEERS-IAENGen_US
dc.relation.ispartofIMECS 2009: INTERNATIONAL MULTI-CONFERENCE OF ENGINEERS AND COMPUTER SCIENTISTS, VOLS I AND IIen_US
dc.relation.ispartofseriesLecture Notes in Engineering and Computer Science
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectBroken rotor barsen_US
dc.subjectrotor faults diagnosisen_US
dc.subjectclassification of rotor faultsen_US
dc.subjectshort time Fourier transformen_US
dc.titleNeural Network Classification and Diagnosis of Broken Rotor Bar Faults by Means of Short Time Fourier Transformen_US
dc.typeConference Objecten_US

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