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An Experimental Comparative Study Between Machine Learning and Signal Processing Techniques to Detect Broken Rotors Bars in Induction Motors

by Cleber Gustavo Dias, Rodrigo Cardozo De Jesus
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 30
Year of Publication: 2022
Authors: Cleber Gustavo Dias, Rodrigo Cardozo De Jesus
10.5120/ijca2022922351

Cleber Gustavo Dias, Rodrigo Cardozo De Jesus . An Experimental Comparative Study Between Machine Learning and Signal Processing Techniques to Detect Broken Rotors Bars in Induction Motors. International Journal of Computer Applications. 184, 30 ( Oct 2022), 1-8. DOI=10.5120/ijca2022922351

@article{ 10.5120/ijca2022922351,
author = { Cleber Gustavo Dias, Rodrigo Cardozo De Jesus },
title = { An Experimental Comparative Study Between Machine Learning and Signal Processing Techniques to Detect Broken Rotors Bars in Induction Motors },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2022 },
volume = { 184 },
number = { 30 },
month = { Oct },
year = { 2022 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number30/32501-2022922351/ },
doi = { 10.5120/ijca2022922351 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:22:45.611737+05:30
%A Cleber Gustavo Dias
%A Rodrigo Cardozo De Jesus
%T An Experimental Comparative Study Between Machine Learning and Signal Processing Techniques to Detect Broken Rotors Bars in Induction Motors
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 30
%P 1-8
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents an experimental comparative study between some machine learning techniques and some signal processing methods to detect broken rotor bars in squirrel cage induction motors (SCIM). It has been used a current transformer to measure the stator current data from only one phase of the machine. The present research have addressed a common operational condition, particularly when motors run at low slip, i.e., with low load. In this work, three pre-processing approaches have been applied, such as the Fast Fourier Transform (FFT), Hilbert Transformation (HT) and some statistical data (SData). The sampled features have been applied to training and validating steps, by using three classification models, such as Support Vector Machine (SVM), k-Nearest Neighbor (KNN) and Logistic Regression (LR), not only to detect the failure, but also to evaluate its severity. The present study also presents a wide discussion about the parameters evaluated for each machine learning technique, in order to demonstrate that different choices can significantly affect the performance of each classifier. The best parameters have been identified for distinct rotor conditions. In addition, the Pearson correlation coefficient has been applied in a further investigation that shown the great possibility to reduce the number of input features and still maintaining a very good performance for the classifiers. The efficiency of this approach was evaluated and tested experimentally from a 7.5-kW induction motor running at low slip using a variable speed drive.

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Index Terms

Computer Science
Information Sciences

Keywords

Broken Rotor Bars Fault Diagnosis Machine Learning Signal Processing Induction Motor

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