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A computation strategy based on neural network for stiffness determination of deep‐groove ball bearings

Yuan Kang (Department of Mechanical Engineering, Chung Yuan Christian University, Taiwan, People's Republic of China)
Ping‐Chen Shen (Department of Mechanical Engineering, Yuan Ze University, Taiwan, People's Republic of China)
Cheng‐Hsien Chen (Department of Refrigeration and Air Conditioning, Chin Min College, Taiwan, People's Republic of China)
Chih‐Ching Huang (System Manufacturing Center, Chung Shan Institute of Science and Technology, Taiwan, People's Republic of China)
Lin‐Kan Yang (Department of Mechanical Engineering, Chung Yuan Christian University, Taiwan, People's Republic of China)

Industrial Lubrication and Tribology

ISSN: 0036-8792

Article publication date: 1 June 2004

473

Abstract

All deep‐groove ball bearings have similar features in geometry, mechanism, and structure. Stiffness of this type of bearings is related to geometry, dimensions, and operating conditions by a very complex, high‐order and coupled‐variable function. This paper has verified that the stiffness function for all deep‐groove ball bearings can be replaced by a back‐propagation neural network (BPNN) which is trained by using some (not all) samples.

Keywords

Citation

Kang, Y., Shen, P., Chen, C., Huang, C. and Yang, L. (2004), "A computation strategy based on neural network for stiffness determination of deep‐groove ball bearings", Industrial Lubrication and Tribology, Vol. 56 No. 3, pp. 147-157. https://doi.org/10.1108/00368790410532183

Publisher

:

Emerald Group Publishing Limited

Copyright © 2004, Emerald Group Publishing Limited

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