Fault Detection and Diagnosis of a 3-Phase Induction Motor Using Kohonen Self-Organising Map

Robert Agyare Ofosu (Jiangsu University)
Benjamin Odoi (University of Mines and Technology)
Daniel Fosu Boateng (University of Mines and Technology)
Asaph Mbugua Muhia (Jomo Kenyatta University of Agriculture and Technology)

JURNAL NASIONAL TEKNIK ELEKTRO
Vol 12, No 1: March 2023

This paper uses the Kohonen Self-Organising Map (KSOM) to detect, diagnose, and classify induction motor faults. A series of simulations using models of the 3-phase induction motor based on real industrial motor parameters were performed using MATLAB/Simulink under fault conditions such as inter-turn, power frequency variation, over-voltage and unbalance in supply voltage. The model was trained using the input signals of the various fault conditions. Various faults from an unseen induction motor were fed to the model to test the model’s ability to detect and classify induction motor faults. The KSOM adapted to the conditions of the unseen motor, detected, diagnosed and classified these faults with an accuracy of 94.12%.