商用车传动轴疲劳寿命数据驱动预测与多参数优化

doi:10.3969/j.issn.1674-8484.2026.02.005

摘要/Abstract

摘要：

为高效且精确地预测与优化车辆传动轴总成的疲劳寿命，提出一种数据驱动方法。以实验测试与有限元分析为基础，利用深度神经网络对关键零部件参数进行优化设计。通过应变实验验证了传动轴总成有限元模型的可靠性，并据此建立了可靠的疲劳寿命计算模型；基于该模型构建了不同载荷与空间夹角等安装条件的疲劳寿命数据集；利用深度神经网络提取特征构建疲劳寿命预测模型；为进一步优化结构寿命，选取了7个关键零部件的近40个设计尺寸构建多参数数据集，基于该神经网络预测模型对关键参数进行协同优化设计。结果表明：经多参数协同优化后，传动轴的整体疲劳寿命显著提升了22.6%，该数据驱动方法能快速、准确地预测并有效优化传动轴疲劳寿命。

关键词: 商用车传动轴, 疲劳寿命预测, 数据驱动, 参数优化

Abstract:

A data-driven method was proposed to efficiently and accurately predict and optimize the fatigue life of vehicle drive shaft assemblies. Based on experimental testing and finite element analysis (FEA), a deep neural network (DNN) was utilized to optimize the parameters of key components. The reliability of the finite element model for the drive shaft assembly was verified through strain testing, based on which a reliable fatigue life calculation model was established. Subsequently, a fatigue life dataset under various installation conditions of different loads and spatial installation angles was constructed with the model. A DNN was then employed to extract features and construct a fatigue life prediction model. To further optimize the structural life, a multi-parameter dataset was constructed by selecting nearly 40 design dimensions from 7 key components, and a collaborative optimization of the key parameters was conducted based on the DNN prediction model. The results show that the overall fatigue life of the drive shaft assembly is significantly improved by 22.6% after the multi-parameter collaborative optimization. The proposed data-driven method can rapidly and accurately predict, as well as effectively optimize, the fatigue life of drive shafts.

Key words: commercial vehicle driveshaft, fatigue life prediction, data-driven, parameter optimization

中图分类号:

U463.216+2

刘祥, 殷玉明, 吴植文, 丁孝禹, 徐华福, 高佳庆, 杨大胜. 商用车传动轴疲劳寿命数据驱动预测与多参数优化[J]. 汽车安全与节能学报, 2026, 17(2): 200-208.

LIU Xiang, YIN Yuming, WU Zhiwen, DING Xiaoyu, XU Huafu, GAO Jiaqing, YANG Dasheng. Data-driven prediction and multi-parameter optimization of fatigue life of commercial vehicle drive shafts[J]. Journal of Automotive Safety and Energy, 2026, 17(2): 200-208.

图/表 15

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应变片序号	最大扭矩时平均应变/ (μm·m^-1)
应变片序号	0°夹角	5°夹角
NO-01-A	352.12	323.27
NO-01-B	-338.91	-284.93
NO-01-C	-255.90	-204.61
NO-02	-302.77	-270.11
NO-03	-337.78	-301.87

应变片序号	最大扭矩时平均应变/ (μm·m^-1)
应变片序号	0°夹角	5°夹角
NO-01-A	352.12	323.27
NO-01-B	-338.91	-284.93
NO-01-C	-255.90	-204.61
NO-02	-302.77	-270.11
NO-03	-337.78	-301.87

应变位置序号	应变/ (μm·m^-1)
应变位置序号	0°夹角	5°夹角
NO-01	597.62	520.07
NO-02	300.81	265.20
NO-03	341.64	308.20

应变位置序号	应变/ (μm·m^-1)
应变位置序号	0°夹角	5°夹角
NO-01	597.62	520.07
NO-02	300.81	265.20
NO-03	341.64	308.20

零件	尺寸变化范围	仿真样本量	隐藏层	RMSE				相关系数R
零件	尺寸变化范围	仿真样本量	隐藏层	训练	验证	测试	总数据	训练	验证	测试	总数据
输入法兰叉	(±20)%	690	40-25-20-15-6	0.200 7	0.243 7	0.254 2	0.216 3	0.856 4	0.731 5	0.730 3	0.819 3
输出法兰叉		688	45-31-20-12-7	0.090 2	0.152 0	0.137 2	0.109 5	0.901 4	0.602 9	0.674 8	0.832 6
焊接叉		834	50-34-25-15-8	0.238 4	0.353 2	0.334 0	0.284 9	0.880 1	0.663 5	0.756 3	0.814 0
花键叉轴		782	60-32-16-8-4	0.064 0	0.182 3	0.231 1	0.125 8	0.983 5	0.847 8	0.721 0	0.930 2
花键套		642	40-24-20-15-6	0.128 0	0.189 3	0.238 6	0.159 3	0.914 8	0.877 9	0.439 2	0.864 3
十字轴		492	40-30-25-15-6	0.114 4	0.160 1	0.201 2	0.138 1	0.953 7	0.918 3	0.900 4	0.936 9
轴管		120	40-24-15-6	0.340 0	0.357 0	0.148 9	0.321 4	0.721 4	0.824 4	0.909 6	0.745 1