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:-
Dr. 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/v10/2
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