A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors

Abstract Three-phase induction motors are the key elements of electromechanical energy conversion for a variety of industrial sectors. The ability to identify motor faults before they occur can reduce the risks in decisions regarding machine maintenance, lower costs, and increase process availability. This article proposes a comprehensive evaluation of pattern classification methods for fault identification in induction motors. The methods discussed in this work are: Naive Bayes, k-Nearest Neighbor, Support Vector Machine (Sequential Minimal Optimization), Artificial Neural Network (Multilayer Perceptron), Repeated Incremental Pruning to Produce Error Reduction, and C4.5 Decision Tree. By analyzing the amplitudes of current signals in the time domain, experimental results with bearing, stator, and rotor faults are tested using different pattern classification methods under varied power supply and mechanical loading conditions.

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