N2 - 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%. L1 - http://www.czasopisma.pan.pl/Content/107264/AoA_122364.pdf L2 - http://www.czasopisma.pan.pl/Content/107264 PY - 2018 IS - No 2 DO - 10.24425/122364 KW - self-aligning bearing KW - fault classification KW - artificial neural networks KW - deep neural networks A1 - Rakesh, Abhishek A1 - Aravind, Arun A1 - Narendiranath, Babu T. A1 - Jahzan, Mohamed A1 - Prabha D., Rama PB - Polish Academy of Sciences, Institute of Fundamental Technological Research, Committee on Acoustics VL - vol. 43 DA - 2018.06.15 T1 - Application of EMD ANN and DNN for Self-Aligning Bearing Fault Diagnosis UR - http://www.czasopisma.pan.pl/dlibra/publication/edition/107264 T2 - Archives of Acoustics