Application of rough sets techniques to induction machine broken bar detection

Abstract

A fault diagnosis system using rough sets based classification techniques is developed for cage induction machines broken bar detection. The proposed algorithm uses the stator current and motor speed as input. Several features are extracted from the frequency spectrum of the current signal resulting from FFT. A Rough Sets based classifier is then developed and applied to distinguish between different motor conditions. A series of experiments using a three phase 3 hp cage induction machine performed in different load and fault conditions are used to provide data for training and then testing the classifier. Experimental results confirm the efficiency of the proposed algorithm for detecting the existence and severity of broken bar faults.