Title: Rotor fault characterisation in induction motors under different load levels via machine learning methods

Authors: Hayri Arabacı; Mücahid Barstuğan

Addresses: Department of Electrical and Electronics Engineering, Faculty of Technology, Selcuk University, Konya, Türkiye ' Department of Electrical and Electronics Engineering, Faculty of Engineering and Natural Sciences, Konya Technical University, Konya, Türkiye

Abstract: Induction motors stand out for their robustness and are widely used in the industrial sector. Literature studies have focused more on rotor faults because rotor fault signatures are hard to detect. In most experimental studies, tests were carried out using a single motor for fault classification. In general, training and fault classification was conducted on a single load type. This study focused on fault classification for induction motors with varying powers and load conditions. Motor current data for four different induction motors and randomly selected load levels were obtained, a classifier structure was formed using machine learning, and tests were carried out. Classification results for the five classifiers were obtained and compared to determine the reliability of the generalised classifier structure. Support vector machines and k-nearest neighbour methods were used in the classification and k-nearest neighbour achieved at 99.51% accuracy.

Keywords: fault classification; induction motor; machine learning; pattern recognition; rotor faults.

DOI: 10.1504/IJAISC.2024.139606

International Journal of Artificial Intelligence and Soft Computing, 2024 Vol.8 No.2, pp.129 - 146

Received: 06 Apr 2023
Accepted: 19 Feb 2024
Published online: 04 Jul 2024 *

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