Scopus
YÖKSİS Eşleşti
Automatic detection and classification of rotor cage faults in squirrel cage induction motor
Neural Computing and Applications · Temmuz 2010
YÖKSİS Kayıtları
Automatic detection and classification of rotor cage faults in squirrel cage induction motor
Neural Computing and Applications · 2010 SCI-Expanded 6 atıf
PROFESÖR HAYRİ ARABACI →
Makale Bilgileri
DergiNeural Computing and Applications
Yayın TarihiTemmuz 2010
Cilt / Sayfa19 · 713-723
Scopus ID2-s2.0-77953912939
Özet
The 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. © Springer-Verlag London Limited 2009.
Yazarlar (2)
1
Hayri Arabaci
2
Osman Bilgin
Anahtar Kelimeler
Fault diagnosis
Fourier analysis
Neural network
Rotor faults
Squirrel cage induction motor
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Metrikler
34
Atıf
2
Yazar
5
Anahtar Kelime