基于随机森林算法的改性水润滑轴承摩擦性能预测

Friction Performance Analysis of Modified Water-lubricated Bearing Based on Random Forest Algorithm

为验证随机森林算法在预测改性水润滑轴承摩擦学性能上的可行性,利用Python编写算法,并通过已知实验数据进行仿真建模。通过已知数据对算法的准确性进行验证,其接受者操作特征(ROC)曲线的均值为0.85,证明模型的准确性较高。在不同温度及载荷工况条件下通过实验对预测模型进行验证,实验结果与预测结果间的误差均在5%左右,表明构建的随机森林模型可以用于改性水润滑轴承的摩擦学性能预测。研究结果表明:温度对于该改性水润滑轴承的平均摩擦因数有较大的影响,而负载对平均摩擦因数的影响较小,但是对于轴承的运转稳定性影响较大。

In order to verify the feasibility of the random forest algorithm in predicting the tribological properties the modified water-lubricated bearing,the algorithm was written by Python,and the simulation modeling was established based on the known data.The accuracy of the algorithm was verified by the known data,and the average value of the receiver operating characteristic(ROC)curve is 0.85,which proved that the accuracy of the model was high.The prediction model was verified by experiments under different temperature and load conditions.The error between the experimental results and the prediction results is about 5%,indicating that the random forest model can be used to predict the tribological performance of modified water lubricated bearings.The results show that the temperature has a great influence on the average friction coefficient of the modified water lubricated bearing,while the load has a small influence on the average friction coefficient,but has a great influence on the running stability of the bearings.