Archives of Acoustics, 43, 2, pp. 163–175, 2018

Application of EMD, ANN and DNN for Self-Aligning Bearing Fault Diagnosis

Narendiranath Babu THAMBA
VIT University

VIT University

Abhishek RAKESH
VIT University

Mohamed JAHZAN
VIT University

Rama Prabha D.
VIT University

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%.
Keywords: self-aligning bearing; fault classification; artificial neural networks; deep neural networks
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Copyright © Polish Academy of Sciences & Institute of Fundamental Technological Research (IPPT PAN).


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DOI: 10.24425/122364