Induction motors are a critical component of many industrial processes and are frequently integrated into commercially available equipment. Safety, reliability, efficiency, and performance are some of the major concerns of induction motors.
Three-phase induction motors are the ‘workhorses’ of
industry and are the most widely used electrical machines. For this reason, the
detection of motor failures is very important. Bearing problems are one of the
major causes of drive failures. Early detection of bearing faults allows
replacements of the bearings rather than replacement of the motor. The present
contribution reports experimental results for monitoring bearing faults in
induction motors. Motor line currents have been analyzed using modern signal
processing and data reduction tools combining 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 a 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. It
is observed that for five processing elements in the hidden layer, 100%
classification accuracy is achieved for healthy and faulty conditions.
Experimental results are presented to demonstrate the
effectiveness of the proposed methodology. The study concluded that the
proposed methodology is useful in identifying inner race and outer race defects
of bearings in induction motors with 100
percent accuracy.
Author (s) Details
Anjali. U Jawadekar
Department of Electrical Engineering, S.S.G.M. College of Engineering
Shegaon, India.
Mukesh Ravindra Chavan
Department of Electrical Engineering, S.S.G.M. College of Engineering
Shegaon, India.
Please see the book here:- https://doi.org/10.9734/bpi/caert/v10/2639
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