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Design of neural network model for analysing hydrostatic circular recessed bearings with axial piston pump slipper

Fazıl Canbulut (Mechanical Engineering Department, Faculty of Engineering, Erciyes University, Kayseri, Turkey)
Cem Sinanoğlu (Mechanical Engineering Department, Faculty of Engineering, Erciyes University, Kayseri, Turkey)
Şahin Yıldırım (Mechanical Engineering Department, Faculty of Engineering, Erciyes University, Kayseri, Turkey)
Erdem Koç (Textile Engineering Department, Faculty of Engineering and Architecture, Çukurova University, Adana, Turkey)

Industrial Lubrication and Tribology

ISSN: 0036-8792

Article publication date: 1 October 2004

Abstract

A neural network is employed to analyze axial piston pump of hydrostatic circular recessed bearing. Owing to complexity of the system, the neural network is used to predict the bearing parameters of the experimental system. The system mainly consists of cylinder block, piston, slipper, ball‐joint and swash plate. The neural model of the system has three layers, which are input layer with one neuron, hidden layer with ten neurons and output layer with three neurons. It can be outlined from the results for both approaches neural network could be modeled bearing systems in real time applications.

Keywords

Citation

Canbulut, F., Sinanoğlu, C., Yıldırım, Ş. and Koç, E. (2004), "Design of neural network model for analysing hydrostatic circular recessed bearings with axial piston pump slipper", Industrial Lubrication and Tribology, Vol. 56 No. 5, pp. 288-299. https://doi.org/10.1108/00368790410550705

Publisher

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Emerald Group Publishing Limited

Copyright © 2004, Emerald Group Publishing Limited