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All Journal JURNAL NASIONAL TEKNIK ELEKTRO
Daniel Fosu Boateng
University of Mines and Technology
Electrical & Electronics Engineering

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Fault Detection and Diagnosis of a 3-Phase Induction Motor Using Kohonen Self-Organising Map Robert Agyare Ofosu; Benjamin Odoi; Daniel Fosu Boateng; Asaph Mbugua Muhia
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 1: March 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n1.1047.2023

Abstract

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%.
Co-Authors Asaph Mbugua Muhia Benjamin Odoi Robert Agyare Ofosu