@ARTICLE{Narendiranath_Babu_T._Automatic_2018,
 author={Narendiranath Babu, T. and Aravind, Arun and Rakesh, Abhishek and Jahzan, Mohamed and Rama Prabha, D. and Ramalinga Viswanathan, Mangalaraja},
 volume={vol. 43},
 number={No 4},
 journal={Archives of Acoustics},
 pages={727–738},
 howpublished={online},
 year={2018},
 publisher={Polish Academy of Sciences, Institute of Fundamental Technological Research, Committee on Acoustics},
 abstract={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.},
 type={Artykuły / Articles},
 title={Automatic Fault Classification for Journal Bearings Using ANN and DNN},
 URL={http://ochroma.man.poznan.pl/Content/109206/PDF/aoa.2018.125166.pdf},
 doi={10.24425/aoa.2018.125166},
 keywords={journal bearing, fault classification, artificial neural networks, deep neural networks},
}