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

Cilt

Sayı

Künye