Automatic Detection and Classification of Rotor Cage Faults in Squirrel Cage Induction Motor

Basit Öğe Kaydı
dc.contributor.authorArabacı, Hayri
dc.contributor.authorBilgin, Osman
dc.date.accessioned2020-03-26T17:47:11Z
dc.date.available2020-03-26T17:47:11Z
dc.date.issued2010
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThe detection of broken rotor bars and broken end-ring in three-phase squirrel cage induction motors by means of improved decision structure. The structure consists of current signal analysis (CSA), Artificial Neural Network (ANN) and diagnosis algorithm. Effects of broken bars and end-ring on current signal and feature extraction are in the CSA. The rotor cage faults are classified by using ANN. And result matrixes of ANN are considered two different ways for diagnosis. Then the diagnoses are compared with each other. In this study six different rotor faults, which are one, two, three broken bars, bar with high resistance, broken end-ring and healthy rotor, are investigated. The effects of different rotor faults on current spectrum, in comparison with other fault conditions, are investigated by analyzing side-bands in current spectrum. To reduce bad effects of changing of distance between the side-band and main component on the detection and classification of the faults, the spectrum is achieved with low definition. Thus, the improved decision structure diagnoses faulted rotors with 100% accuracy and classified rotor faults 98.33% accuracy.en_US
dc.description.sponsorshipSelcuk UniversitySelcuk Universityen_US
dc.description.sponsorshipThis study has been supported by Scientific Research Project of Selcuk University.en_US
dc.identifier.citationArabacı, H., Bilgin, O., (2010). Automatic Detection and Classification of Rotor Cage Faults in Squirrel Cage Induction Motor. Neural Computing & Applications, 19(5), 713-723. Doi: 10.1007/s00521-009-0330-7
dc.identifier.doi10.1007/s00521-009-0330-7en_US
dc.identifier.endpage723en_US
dc.identifier.issn0941-0643en_US
dc.identifier.issn1433-3058en_US
dc.identifier.issue5en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage713en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s00521-009-0330-7
dc.identifier.urihttps://hdl.handle.net/20.500.12395/24641
dc.identifier.volume19en_US
dc.identifier.wosWOS:000278837800007en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorArabacı, Hayri
dc.institutionauthorBilgin, Osman
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofNeural Computing & Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectSquirrel cage induction motoren_US
dc.subjectFault diagnosisen_US
dc.subjectRotor faultsen_US
dc.subjectNeural networken_US
dc.subjectFourier analysisen_US
dc.titleAutomatic Detection and Classification of Rotor Cage Faults in Squirrel Cage Induction Motoren_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
4641.pdf
Boyut:
833.7 KB
Biçim:
Adobe Portable Document Format
Açıklama:
Makale Dosyası
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Elektirik ve Elektronik Mühendisliği/Makale Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu