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

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