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基于大数据和动力学模型的车轮磨耗预测研究
Research on Wheel Wear Prediction Based on Big Data and Dynamic Model

DOI: 10.12677/hjdm.2024.142010, PP. 116-124

Keywords: 车轮磨耗,大数据分析,机车动力学模型,磨耗系数k
Wheel Wear
, Big Data Analysis, Locomotive Dynamics Model, Wear Coefficient k

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Abstract:

为了研究车轮磨耗的特点和演变过程,对机车车轮的磨耗进行了大数据分析研究发现:轮径及轮缘厚度的磨耗率随轮径值及轮缘厚度的降低呈先减小后增大的趋势、且新轮状态下车轮踏面的磨耗率约为轮缘磨耗率的三倍左右;新轮状态下车轮踏面磨耗较为明显,一定运行里程后轮缘磨耗更为突出。建立了机车动力学模型、轮轨滚动接触模型、材料磨损模型一体的车轮磨耗计算模型,并使用实测车轮数据与优化算法相结合的方式来对车轮磨耗计算模型中磨耗系数k进行优化,计算发现:磨耗系数取平均值的车轮磨耗计算结果与实测值误差较大,而取优化值的计算结果与实测值的误差较小在3%~13%之间(车轮磨耗集中在?45~40 mm,磨耗最大位置在?10~?5 mm之间)。
In order to study the characteristics and evolution process of wheel wear, the big data analysis of locomotive wheel wear was carried out, and it was found that: The abrasion rate of wheel diameter and rim thickness decreases first and then increases with the decrease of wheel diameter and rim thickness, and the wear rate of wheel tread is about three times that of the rim wear rate under the new wheel state; The wear of the wheel tread is more obvious in the new wheel state, and the wear of the wheel rim is more prominent after a certain mileage of operation. A wheel wear calculation model integrating the locomotive dynamics model, wheel-rail rolling contact model and material wear model was established. The wear coefficient k in the wheel wear calculation model is optimized by combining the measured wheel data and the optimization algorithm. The results show that the error between the calculated results of wheel wear and the measured value with the average value of the wear coefficient is larger, while the error between the calculated result and the measured value with the optimized value is smaller between 3%~13% (the wheel wear is concentrated in ?45~40 mm, and the maximum wear position is between ?10~?5 mm).

References

[1]  杜彬, 胡军海, 宋冬利. 重载铁路货车车轮踏面磨耗表征方法及其规律分析[J]. 铁道机车车辆, 2022, 42(1): 1-9.
[2]  邹强, 江波, 张华, 等. 基于SIMPACK的大功率机车车轮踏面损伤预测[J]. 机械, 2022, 49(5): 33-40.
[3]  于春广, 陶功权. 地铁车轮磨耗测试及数值仿真[J]. 工程力学, 2016, 33(1): 201-208, 245.
[4]  李霞, 金学松, 温泽峰, 等. 计算铁路车轮轮周磨耗量的两种方法对比[J]. 工程力学, 2011, 28(1): 205-211, 218.
[5]  黄宇峰, 曾京, 汪群生, 等. 高速列车车轮型面磨耗预测及参数研究[J]. 中国铁路, 2018(3): 93-98.
[6]  周迅, 喻财栋, 杨柳青, 等. 基于ALE法的车轮踏面磨损及疲劳性能研究[J]. 机械强度, 2018, 40(1): 183-188.
[7]  赵新光. HXD2C型机车车轮磨耗问题研究[J]. 电力机车与城轨车辆, 2022, 45(5): 122-126 134.
[8]  华莎. 基于数据智能分析的列车车轮磨耗预测与镟修策略研究[D]: [硕士学位论文]. 南京: 南京航空航天大学, 2017.
[9]  张曙光. HXD1型电力机车[M]. 北京: 中国铁道出版社, 2009: 5-9.
[10]  Zhu, A.H., Yang, S., Li, Q., et al. (2019) Simulation and Measurement Study of Metro Wheel Wear Based on the Archard Model. Industrial Lubrication and Tribology, 71, 284-292.
https://doi.org/10.1108/ILT-01-2018-0045
[11]  国家铁路局. 机车车辆车轮轮缘踏面外形: TB/T449-2016 [S]. 北京: 中国铁道出版社, 2016.
[12]  方鑫, 刘通, 程亚萍, 等. 基于GA-岭回归分析的机车车轮踏面磨耗量预测算法研究[J]. 机车电传动, 2023(6): 71-78.
[13]  朱爱华. 地铁车轮磨耗及其对动力学性能影响的研究[D]: [博士学位论文]. 北京: 北京交通大学, 2020.
[14]  肖国放, 陶功权, 刘孟奇, 等. 地铁车辆车轮偏磨原因分析与对策研究[J]. 机械工程学报, 2020, 56(22): 247-255.

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