Predictions of Minimum Fluid Film Thickness of Journal Bearing Using Feed-Forward Neural Network-Reference-Cited by-同舟云学术

Predictions of Minimum Fluid Film Thickness of Journal Bearing Using Feed-Forward Neural Network

Published:2020 Issue: Volume: Page:229-237
ISSN:2190-3018
Container-title:Proceedings of International Conference in Mechanical and Energy Technology
language:
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Author:

Kumar Sunil^ORCID,Kumar Vijay,Singh Anoop Kumar

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-15-2647-3_21

Reference11 articles.

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2. Sinanoğlu, C.: Design of neural model for analysing journal bearings considering effects of transverse and longitudinal profile. Ind. Lubr. Tribol. 61(3), 132–139 (2009)

3. Patel, P.M., Prajapati, J.M.: A review on artificial intelligent system for bearing condition monitoring. Int. J. Eng. Sci. Technol. 3(2), 1520–1525 (2011)

4. Garg, H.C., Sharda, H.B., Kumar, V.: On the design and development of hybrid journal bearings: a review. Tribotest 12(1), 1–19 (2006)

5. Ram, N.: Effect of couple stress lubrication on symmetric hole-entry hybrid journal bearing. Tribol. Online 12(2), 58–66 (2017)

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