Paper Title : Fault Diagnostics of Rolling Bearing based on Improve Time and
Frequency Domain Features using Artificial Neural Networks
Author Designation : 1Associate Professor 2Research Scholar 3Assistant Professor
Author College Name : 1KIRC, Kalol 2KSV, Gandhinagar 3CSPIT, Changa
Abstract - 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.
Keyword - 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.
For Full Length Paper Visit http://www.ijsrd.com/Article.php?manuscript=IJSRDV1I4003
