Performance comparison of classifiers in the detection of short circuit incipient fault in a three-phase induction motor

This paper aims at the detection of short-circuit incipient fault condition in a three-phase squirrel-cage induction motor fed by a sinusoidal PWM converter. In order to detect this fault, different operation conditions are applied to an induction motor, and each sample of the real data set is taken from the line currents of the PWM converter aforementioned. For feature extraction, the Motor Current Signature Analysis (MCSA) is used. The detection of this fault is treated as a classification problem, therefore different supervised algorithms of machine learning are used so as to solve it: Multi-layer Perceptron (MLP), Extreme Learning Machine (ELM), Support-Vector Machine (SVM), Least-Squares Support-Vector Machine (LSSVM), and the Minimal Learning Machine (MLM). These classifiers are tested and the results are compared with other works with the same data set. In near future, an embedded system can be equipped with these algorithms.