The Performance Evaluation of Machine Learning based Techniques via Stator Current and Stray Flux for Broken Bar Fault in Induction Motors
Küçük Resim Yok
Tarih
2021
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this paper, the machine learning based techniques are evaluated using stator current and stray flux for broken bar fault in induction motors (IMs). The feature extraction is achieved from Discrete Wavelet Transform (DWT) for both healthy and faulty operations. In order to analyze the performance of different classifier, six fundamental classifications with 23 sub-classifiers are used via a toolbox. It has been observed that 18 out of 23 classifiers have shown great performance (100% accuracy) and two more classifier results at accuracy of greater than 90% for stray flux. Both simulation and experimental results show that stray flux provides better diagnostics results than stator current using different machine learning based classification algorithms in IMs.
Açıklama
13th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED) -- AUG 22-25, 2021 -- Dallas, TX
Anahtar Kelimeler
Broken rotor bar fault, discrete wavelet transform, fault diagnosis, induction motor, machine learning techniques
Kaynak
2021 Ieee 13th International Symposium on Diagnostics For Electrical Machines, Power Electronics and Drives (Sdemped)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A