Rotor fault characterisation in induction motors under different load levels via machine learning methods Online publication date: Thu, 04-Jul-2024
by Hayri Arabacı; Mücahid Barstuğan
International Journal of Artificial Intelligence and Soft Computing (IJAISC), Vol. 8, No. 2, 2024
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.
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