NOVEL WAVELET ANN TECHNIQUE TO CLASSIFY BEARING FAULTS IN THREE PHASE INDUCTION MOTOR

Three phase induction motors are the ‘workhorses’ of industry and are the most widely used electrical machines. For this reason detection of motor failures is very important. Bearing problems is one of the major causes for drive failures. Early detection of bearing faults allows replacements of the bearings rather than replacement of motor. Present contribution reports experimental results for monitoring of bearing faults in induction motor. Motor line currents have been analyzed using modern signal processing and data reduction tool combing Park’s Transformation and Discrete Wavelet Transform (DWT). Feed Forward Artificial Neural (FFANN) based data classification tool is used for fault characterization based on DWT features extracted from Park’s Current Vector Pattern. An online algorithm is tested successfully on three phase induction motor and experimental results are presented to demonstrate the effectiveness of the proposed method which can reliably distinguish the inner race and outer race defects of the bearing. Experimental results are presented to demonstrate the effectiveness of the proposed method.