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

ZHANG Tao; GAO Huimin; YU Fanzhen; YANG Kun

doi:10.19693/j.issn.1673-3185.02685

Volume 17 Issue 6

Dec. 2022

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Chinese Journal of Ship Research > 2022 > 17(6): 133-140, 147. > DOI: 10.19693/j.issn.1673-3185.02685 CSTR: 32390.14.j.issn.1673-3185.02685

ZHANG T, GAO H M, YU F Z, et al. Performance decay of stern bearing based on lubrication numerical model and state parameters[J]. Chinese Journal of Ship Research, 2022, 17(6): 133–140, 147. DOI: 10.19693/j.issn.1673-3185.02685

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

1.
School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
2.
School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China

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Received Date: December 02, 2021
Revised Date: April 11, 2022
Available Online: April 19, 2022

This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Abstract

Objective This paper puts forward a method for monitoring and evaluating the lubrication performance of a marine stern tube bearing which combines a lubrication performance decay model and support vector machine (SVM) algorithm.
Methods Aiming at difficulties in the monitoring and recognition of the lubrication regimes of stern bearings, a bearing lubrication decay numerical model is established and validated with experimental data. The effects of load, roughness and radius clearance on the lubrication decay mechanism are then investigated. Based on the SVM algorithm, a lubrication regime classifier is constructed; the hyperparameters are optimized through a grid search algorithm; the datasets of different lubrication regimes are used for training; and lubrication regimes for stern bearings are evaluated.
Results The results show that with the increase of external load, roughness and radius clearance, the critical speed of the deterioration of the bearing lubrication regime increases, the working range of hydrodynamic lubrication (HL) decreases and the working range of mixed lubrication（ML） increases. The lubrication regime recognition model is then verified by the simulation dataset, and the proposed lubrication regime recognition method has an accuracy rate of 96.88%.
Conclusion The method proposed herein can monitor the lubrication performance characteristics of marine stern tube bearings and effectively identify the optimal bearing lubrication regime.
- stern bearing,
- lubrication performance,
- support vector machine (SVM),
- state evaluation

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

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