高速列车车轮磨耗对踏面等效锥度的影响研究

doi:10.3901/JME.2025.24.339

摘要/Abstract

摘要： 为了研究高速列车车轮磨耗量与踏面等效锥度之间的关系，利用UIC519积分法结合偏最小二乘法、反向传播神经网络和岭回归模型，对高速列车LMA型踏面的磨耗量和等效锥度之间的关系进行了定性与定量的分析和定性验证，得到了基于偏最小二乘法、反向传播神经网络和岭回归模型的磨耗量与等效锥度的数学关系模型。研究结果表明，岭回归模型更利于分析高速列车车轮磨耗与等效锥度间的相互关联关系，对高速列车磨耗对等效锥度的影响表达更有效。进一步分析发现，当车轮磨耗量达到0.42～0.84 mm时，对应高速列车运行里程为5.7万～12.2万km，车轮踏面名义滚动圆附近出现凹磨现象，显著影响了等效锥度的变化，使建模结果在该阶段误差较大，证明了影响磨耗与等效锥度之间关联关系的最大影响因素为车轮踏面的凹磨。

关键词: 车轮磨耗, 等效锥度, 偏最小二乘法, 反向传播神经网络, 岭回归模型

Abstract: To study the relationship between wheel wear and equivalent conicity of high-speed trains, the UIC519 integration method is applied in combination with partial least square, backpropagation neural network, and Ridge Regression models. The wear of LMA-type wheel profile and its relationship with equivalent conicity are analyzed both qualitatively and quantitatively, resulting in the establishment and validation of mathematical models based on these three methods. The study results indicate that the Ridge Regression model is more suitable for analyzing the correlation between wheel wear and equivalent conicity in high-speed trains, effectively capturing the impact of wear on equivalent conicity. Further analysis shows that when the wheel wear reaches 0.42～0.84 mm, corresponding to a mileage of approximately 57 000 km to 122 000 km for high-speed trains, concave wear is observed near the nominal rolling circle of the wheel profile. This phenomenon significantly influences the variation in equivalent conicity, leading to larger modeling errors in this range. It is further demonstrated that the primary factor affecting the relationship between wheel wear and equivalent conicity is the concave wear of the wheel profile.

Key words: wheel wear, equivalent conicity, partial least squares regression, back-propagation neural network, ridge regression model

中图分类号:

U270

肖乾, 崔正淳, 徐中旭, 申悦. 高速列车车轮磨耗对踏面等效锥度的影响研究[J]. 机械工程学报, 2025, 61(24): 339-350.

XIAO qian, Cui zhengchun, XU Zhongxu, SHEN Yue. Research on The Effect of Wheel Wear on Equivalent Conicity of High-Speed Trains[J]. Journal of Mechanical Engineering, 2025, 61(24): 339-350.

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