Abstract
Self-aligning roller bearings are an integral part of the industrial machinery. The proper analysis and
prediction of the various faults that may happen to the bearing beforehand contributes to an increase
in the working life of the bearing. This study aims at developing a novel method for the analysis of
the various faults in self-aligning bearings as well as the automatic classification of faults using artificial
neural network (ANN) and deep neural network (DNN). The vibration data is collected for six different
faults as well as for the healthy bearing. Empirical mode decomposition (EMD) followed by Hilbert Huang
transform is used to extract instantaneous frequency peaks which are used for fault analysis. Time domain
and time-frequency domain features are then extracted which are used to implement the neural networks
through the pattern recognition tool in MATLAB. A comparative study of the outputs from the two
neural networks is also performed. From the confusion matrix, the efficiency of the ANN has been found
to be 95.7% and using DNN has been found to be 100%.
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