Automatic Detection and Classification of Rotor Cage Faults in Squirrel Cage Induction Motor
Yükleniyor...
Dosyalar
Tarih
2010
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer London Ltd
Erişim Hakkı
info:eu-repo/semantics/openAccess
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.
Açıklama
Anahtar Kelimeler
Squirrel cage induction motor, Fault diagnosis, Rotor faults, Neural network, Fourier analysis
Kaynak
Neural Computing & Applications
WoS Q Değeri
Q4
Scopus Q Değeri
Q1
Cilt
19
Sayı
5
Künye
Arabacı, 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
