国外文献 > 1.轴承 > 2. 轴承类型与部件 > 滑动轴承
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| 标 题： |
A journal bearing performance prediction method utilizing a machine learning technique
一种利用机器学习技术的滚动轴承性能预测方法 |
| 作 者： |
Georgios N Rossopoulos
Christos I Papadopoulos |
| 关键词： |
Journal bearings [滚动轴承]
Hydrodynamic lubrication [ 流体动力润滑]
Operational state prediction [ 运行状态预测]
Journal bearing performance [ 滚动轴承性能]
Lift-off speed [ 起飞速度]
Machine learning [ 机器学习]
Predictive decision making [ 预测性决策]
Feature selection [ 特征选择] |
| 出版年： |
2022 |
| 期刊名： |
Proceedings of the Institution of Mechanical Engineers
机械工程师学会会刊 |
| 年卷期： |
2022, vol.236, no.10 |
| 摘 要： |
A predictive analytics methodology is presented, utilizing machine learning algorithms to identify the performance state of marine journal bearings in terms of maximum pressure, minimum film thickness, Sommerfeld number, load and shaft speed. A dataset of different bearing operation states has been generated by solving numerically the Reynolds equation in the hydrodynamic lubrication regime, for steady-state loading conditions and assuming isothermal and isoviscous lubricant flow. The shaft has been modelled with four different values of misalignment angle, lying within the acceptable operating range, as defined in the existing regulatory framework. The journal bearing was modelled parametrically using generic geometric parameters of a marine stern tube bearing. The lift-off speed was estimated for each loading scenario to ensure operation in the hydrodynamic lubrication regime and the effect of shaft misalignment on lift-off speed has been evaluated. The generated dataset was utilised for training, testing and validation of several machine learning algorithms, as well as feature selection analysis, in order to solve several classification problems and identify the various bearing operational states.
提出了一种预测性分析方法，利用机器学习算法识别船舶滚动轴承在最大压力、最小油膜厚度、索末费尔德数、载荷和轴转速方面的性能状态。通过数值求解流体动力润滑区域内的雷诺方程，在稳态载荷条件下假设润滑油流动为等温且黏度恒定，生成了不同轴承运行状态的数据集。轴的模型包含了四个不同的偏心角值，这些角度均处于现有法规框架所规定的可接受运行范围内。滚动轴承采用船舶艉管轴承的一般几何参数进行参数化建模。针对每种载荷工况估计了脱离速度，以确保轴承在流体动力润滑区域内运行，并评估了轴偏心对脱离速度的影响。生成的数据集被用于训练、测试和验证多种机器学习算法，以及特征选择分析，以解决各类分类问题并识别轴承的各种运行状态。 |
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