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Factors influence the lubrication characteristics investigation and optimization of bearing based on neural network

Zhenpeng He (Civil Aviation University of China, Tianjin, China)
Wenqin Gong (Computer Science and Technology of Tianjin University, Ren’Ai College, Tianjin, China)

Industrial Lubrication and Tribology

ISSN: 0036-8792

Article publication date: 11 April 2016

146

Abstract

Purpose

This paper aims to give the guidance for the design of the bearing.

Design/methodology/approach

The finite element method, the multi-body dynamics method, the finite difference method and the tribology are combined to analyze the lubrication.

Findings

The performance parameters of crankshaft-bearing system such as the misalignment, the oil filling ratio and the oil groove are also investigated. Misalignment causes the pressure to incline on one side and the pressure increases obviously. Filling ratio has great relationship with pressure distribution; the factors influencing the filling ratio are also analyzed. Different oil groove models are investigated, as it can provide the theory for oil groove design, and three factors above are always combined to influence the lubrication characteristics.

Originality/value

The optimization of bearing system is conducted by orthogonal test and neural network, unlike the linear optimization theory. Neural network uses the nonlinear theory to optimize crankshaft-bearing system.

Keywords

Acknowledgements

This project was supported by National Hi-tech Research and Development Program of China (863 Program, Grant No. 2014AA0415011), the National Natural Science Foundation of China (Grant No. 51505482), the Fundamental Research Funds for the Central Universities (No. 3122015C015), Research Fund for the Doctoral Program of Higher Education of China (No. 2014QD02S) and the Doctoral Program of Higher Education of China (No. 2013QD03S).

Citation

He, Z. and Gong, W. (2016), "Factors influence the lubrication characteristics investigation and optimization of bearing based on neural network", Industrial Lubrication and Tribology, Vol. 68 No. 3, pp. 369-385. https://doi.org/10.1108/ILT-07-2015-0093

Publisher

:

Emerald Group Publishing Limited

Copyright © 2016, Emerald Group Publishing Limited

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