Evaluation of stator winding faults severity in inverter-fed induction motors

• Present a comprehensive evaluation of intelligent classifiers to identify stator faults in inverter-fed induction motors are presented.
• Proposed methodology uses the current signal in time domain as the inputs of the pattern classifiers for fault diagnosis.
• Experimental results with different inverters, operating frequencies and mechanical loads are presented.
• Three different intelligent methods are presented and compared for multiple faults under dynamic sampling rate.

Three-phase induction motor are one of the most important elements of electromechanical energy conversion in the production process. However, they are subject to inherent faults or failures under operating conditions. The purpose of this paper is to present a comparative study among intelligent tools to classify short-circuit faults in stator windings of induction motors operating with three different models of frequency inverters. This is performed by analyzing the amplitude of the stator current signal in the time domain, using a dynamic acquisition rate according to machine frequency supply. To assess the classification accuracy across the various levels of faults severity, the performance of three different learning machine techniques were compared: (i) fuzzy ARTMAP network; (ii) multilayer perceptron network; and (iii) support vector machine. Results obtained from 2.268 experimental tests are presented to validate the study, which considered a wide range of operating frequencies and load conditions.

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