Stator fault analysis of three-phase induction motors using information measures and artificial neural networks

- Novel method to identify stator short circuit fault in three-phase induction motors.
- Measures of mutual information between current signals as feature vectors.
- Experimental tests with motors with different voltage unbalance and load conditions.
- Two different neural network topologies are presented and compared as classifiers.

The three-phase induction motors are considered one of the most important elements of the industrial process. However, in this environment, these machines are subject to electrical and mechanical faults, which may cause significant financial losses. Thus, the purpose of this paper is to present a pattern recognition method for the detection of stator windings short circuits based on measures of mutual information between the phase current signals. In order to validate the proposed patterns, feature vectors obtained from normal and faulty motors are applied to two topologies of artificial neural networks. The classification results presented accuracies over 93% even when the motors were subject to several conditions of load torque and power supply voltage unbalance.

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