N2 - Journal bearings are the most common type of bearings in which a shaft freely rotates in a metallic sleeve. They find a lot of applications in industry, especially where extremely high loads are involved. Proper analysis of the various bearing faults and predicting the modes of failure beforehand are essential to increase the working life of the bearing. In the current study, the vibration data of a journal bearing in the healthy condition and in five different fault conditions are collected. A feature extraction method is employed to classify the different fault conditions. Automatic fault classification is performed using artificial neural networks (ANN). As the probability of a correct prediction goes down for a higher number of faults in ANN, the method is made more robust by incorporating deep neural networks (DNN) with the help of autoencoders. Training was done using the scaled conjugate gradient algorithm and the performance was calculated by the cross entropy method. Due to the increased number of hidden layers in DNN, it is possible to achieve a high efficiency of 100% with the feature extraction method. L1 - http://www.czasopisma.pan.pl/Content/109206/PDF/aoa.2018.125166.pdf L2 - http://www.czasopisma.pan.pl/Content/109206 PY - 2018 IS - No 4 EP - 727–738 DO - 10.24425/aoa.2018.125166 KW - journal bearing KW - fault classification KW - artificial neural networks KW - deep neural networks A1 - Narendiranath Babu, T. A1 - Aravind, Arun A1 - Rakesh, Abhishek A1 - Jahzan, Mohamed A1 - Rama Prabha, D. A1 - Ramalinga Viswanathan, Mangalaraja PB - Polish Academy of Sciences, Institute of Fundamental Technological Research, Committee on Acoustics VL - vol. 43 DA - 2018.12.27 T1 - Automatic Fault Classification for Journal Bearings Using ANN and DNN SP - 727–738 UR - http://www.czasopisma.pan.pl/dlibra/publication/edition/109206 T2 - Archives of Acoustics