Rotor Bar Fault Diagnosis by Using Power Factor
Küçük Resim Yok
Tarih
2011
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
INT ASSOC ENGINEERS-IAENG
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The paper presents detection and classification of rotor bar faults at steady state operation in squirrel cage induction motor by using power factor. One phase current and voltage of the stator coils were used to calculate the power factor. To investigate effects of rotor faults on the power factor, its frequency spectrum was obtained by fast Fourier Transform (FFT). Significant picks in the spectrum were used to discern "healthy" and "faulty" motor conditions. The motor conditions were classified by Artificial Neural Network (ANN). In experiments three different rotor faults and healthy motor conditions were investigated by 30 HP, 8", with 18 bars, 380V, 2 poles and 50 Hz squirrel cage submersible induction motor. The proposed decision structure detects and classifies rotor bar faults with 100% accuracy.
Açıklama
World Congress on Engineering (WCE 2011) -- JUL 06-08, 2011 -- Imperial Coll, London, UNITED KINGDOM
Anahtar Kelimeler
Broken rotor bar, fault diagnosis, induction motors, Fast Fourier Transform, Artificial Neural Network
Kaynak
WORLD CONGRESS ON ENGINEERING, WCE 2011, VOL II
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
