TY  - JOUR
ID  - SisLab4547
UR  - http://dx.doi.org/10.30534/ijatcse/2021/031032021
IS  - 3
A1  - Tran, Thanh Huong
A1  - Liang, Hongwei
A1  - Li, Wei
A1  - Pham, Minh Trien
Y1  - 2021///
N2  - This quantitive researchwas conducted to compare the
efficiency between three groups of machine learning?
classification techniques for detecting broken rotor bar
(BRB)fault in induction motor using stator currents signals
with two different signal processing method. Thus, the main
purpose of the article is to find out the most suitable method of
distributing and extracting data for the fault diagnosis
problems. Two of the most common used signal processing
method ? Discrete Wavelet Transform (DWT) and Fast
Fourier Transform (FFT)has been implemented to extract the
statiscal features of the faults. Then in the next logical steps,
also the most important step, fault diagnosis, three
classification algorithms: Support Vector Machines (SVM),
K-nearest Neighbors (KNN), and Ensembles are chosen to
evaluate the performance and the impact of those different
classifiers for induction motor fault diagnosis. Hence, the
study found there are five classifiers (Fine Gaussian SVM,
Fine KNN, Weighted KNN, Bagged Trees and Subspace
KNN) are best suited for the proposed problem when
providing nearly 100% classification accuracy for all fault that
the other 12 classifiers can not perform well.
JF  - International Journal of Advanced Trends in Computer Science and Engineering
VL  - 10
SN  - 22783091
TI  - Comparison of Machine Learning Algorithms for Induction MotorRotor SingleFault Diagnosis using Stator Current Signal
SP  - 1509
AV  - public
EP  - 1514
ER  -