Evaluation of electrical insulation in three-phase induction motors and classification of failures using neural networks

Abstract This paper presents a study for the evaluation of the electrical insulation of the stator of three-phase induction motors (IM) and the classification of the failure mechanism using an approach based on computational intelligence tools (CIT). A brief review showing the main parameters for the evaluation of insulation condition and testing of IMs is presented, as well as the promising use of CITs for fault diagnosis of industrial equipment, including motors. This paper proposes a new methodology for evaluation and classification of insulation conditions with the aid of K-means clustering and of a classifier based on ANNs (artificial neural networks).