A robust linear discrimination fault classification scheme for three phase induction motors

Abstract

In this article a fault classification algorithm based on a robust linear discrimination scheme, for the case of a squirrel-cage three phase induction motor, will be presented. The suggested scheme is based on a novel feature extraction mechanism from the measured magnitude and phase of current park's vector pattern. The proposed methodology has the merit to diagnose different types of faults such as: (a) broken rotor bar, and (b) short circuit in stator winding. The novel feature generation technique is able to transform the problem of fault detection and diagnosis into a simpler space, where direct robust linear discrimination can be applied for solving the classification problem. Robust linear discrimination has been one of the most widely used fault detection method in real life applications, as this methodology seeks for directions that are efficient for discrimination and at the same time requires a straight forward implementation. The efficacy of the proposed scheme will be evaluated based on multiple simulation results for different fault types.