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The analysis of the effects of surface roughness of shafts on journal bearings using recurrent hybrid neural network

The analysis of the effects of surface roughness of shafts on journal bearings using recurrent... This paper presents an investigation for analysing the load carrying capacity of journal bearing in a variety of conditions using a proposed neural network (NN). The NN structure is very suitable for this kind of system. The network is capable of predicting the pressures of the experimental system. The network has parallel structure and fast learning capacity. It can be outlined from the results for both approaches, NN could be used to model journal bearing systems in real time applications. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Industrial Lubrication and Tribology Emerald Publishing

The analysis of the effects of surface roughness of shafts on journal bearings using recurrent hybrid neural network

Industrial Lubrication and Tribology , Volume 56 (6): 10 – Dec 1, 2004

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Publisher
Emerald Publishing
Copyright
Copyright © 2004 Emerald Group Publishing Limited. All rights reserved.
ISSN
0036-8792
DOI
10.1108/00368790410558239
Publisher site
See Article on Publisher Site

Abstract

This paper presents an investigation for analysing the load carrying capacity of journal bearing in a variety of conditions using a proposed neural network (NN). The NN structure is very suitable for this kind of system. The network is capable of predicting the pressures of the experimental system. The network has parallel structure and fast learning capacity. It can be outlined from the results for both approaches, NN could be used to model journal bearing systems in real time applications.

Journal

Industrial Lubrication and TribologyEmerald Publishing

Published: Dec 1, 2004

Keywords: Mechanical components; Neural nets; Surface texture

References