Chinese Journal of Ship Research, Volume. 17, Issue 6, 133(2022)

Performance decay of stern bearing based on lubrication numerical model and state parameters

Tao ZHANG1, Huimin GAO2, Fanzhen YU1, and Kun YANG1
Author Affiliations
  • 1School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
  • 2School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China
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    References(14)

    [6] [6] JIANG L Y, XUE C G, CUI J G, et al. Research recognition of aircraft engine abnmal state[C]The 27th Chinese Control Decision Conference (2015 CCDC). Qingdao, China: IEEE, 2015: 4625–4630.

    [7] YU H J, WEI H J, LI J M et al. Lubrication state recognition based on energy characteristics of friction vibration with EEMD and SVM[J]. Shock and Vibration, 2021, 9972119(2021).

    [9] SOBIE C, FREITAS C, NICOLAI M. Simulation-driven machine learning: bearing fault classification[J]. Mechanical Systems and Signal Processing, 99, 403-419(2018).

    [16] [16] STACHOWIAK G W, BATCHEL A W. Engineering tribology[M]. Oxfd: ButterwthHeinemann, 2013: 322–325.

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    Tao ZHANG, Huimin GAO, Fanzhen YU, Kun YANG. Performance decay of stern bearing based on lubrication numerical model and state parameters[J]. Chinese Journal of Ship Research, 2022, 17(6): 133

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    Paper Information

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    Received: Dec. 3, 2021

    Accepted: --

    Published Online: Mar. 26, 2025

    The Author Email:

    DOI:10.19693/j.issn.1673-3185.02685

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