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Fault Diagnostics of Rolling Bearing based on Improve Time and Frequency Domain Features using Artificial Neural Networks


Dr. Jigar Patel , KIRC, Kalol; Amit Patel, CSPIT, Changa; Vaishali Patel, KSV, Gandhinagar


artificial neural networks (ANNs), condition monitoring, features extraction, Root mean square, Crest factor, Kurtosis, Skewness, Clearance factor, Impulse factor, shape factor, entropy, energy, upper bound, lower bound, central moment, signal distribution1, spectral skewness, spectral kurtosis, spectral energy, Periodogram.


The neural network based approaches a feed forward neural network trained with Back Propagation technique was used for automatic diagnosis of defects in bearings. Vibration time domain signals were collected from a normal bearing and defective bearings under various speed conditions. The signals were processed to obtain various statistical parameters, which are good indicators of bearing condition, then best features are selected from graphical method and these inputs were used to train the neural network and the output represented the bearing states. The trained neural networks were used for the recognition of bearing states. The results showed that the trained neural networks were able to distinguish a normal bearing from defective bearings with 83.33 % reliability. Moreover, the network was able to classify the bearings into different states with success rates better than those achieved with the best among the state-of-the-art techniques.

Other Details

Paper ID: IJSRDV1I4003
Published in: Volume : 1, Issue : 4
Publication Date: 01/07/2013
Page(s): 816-823

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