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1.重庆大学 机械与运载工程学院,重庆 400044
2.重庆大学 高端装备机械传动全国重点实验室,重庆 400044
3.重庆市轨道交通(集团)有限公司,重庆 400014]
余文念,男,1988年生,湖北武汉人,博士,研究员,博士研究生导师;主要研究方向为齿轮系统动力学、信号处理和状态监测等;wennian.yu@cqu.edu.cn。
陈恺 (通信作者),男,1988年生,湖北黄石人,硕士,高级工程师;主要研究方向为轨道车辆;E-mail:18375822876@163.com。
收稿:2024-11-30,
修回:2025-02-18,
纸质出版:2026-03-15
移动端阅览
余文念,旦佳国,陈恺,等. 基于组合代理模型的城轨车辆牵引齿轮传动系统振动优化研究[J]. 机械传动,2026,50(3):70-79.
YU Wennian,DAN Jiaguo,CHEN Kai,et al. Research on vibration response optimization of the urban rail vehicle traction gear transmission system based on combined surrogate model[J]. Journal of Mechanical Transmission,2026,50(3):70-79.
余文念,旦佳国,陈恺,等. 基于组合代理模型的城轨车辆牵引齿轮传动系统振动优化研究[J]. 机械传动,2026,50(3):70-79. DOI: 10.16578/j.issn.1004.2539.2026.03.009.
YU Wennian,DAN Jiaguo,CHEN Kai,et al. Research on vibration response optimization of the urban rail vehicle traction gear transmission system based on combined surrogate model[J]. Journal of Mechanical Transmission,2026,50(3):70-79. DOI: 10.16578/j.issn.1004.2539.2026.03.009.
目的
2
针对城轨车辆牵引齿轮传动系统振动过大,且优化设计中存在运行时间长、收敛速度慢的问题,提出基于组合代理模型的多目标优化方法,实现系统振动特性精准改善与优化效率提升。
方法
2
首先,采用集总参数法建立系统机电耦合动力学模型,获取振动响应数据,为后续优化奠定基础;其次,以结构参数为输入、振动响应为输出,构建融合Kriging、径向基函数法(Radial Basis Function Method
RBF)、多项式响应面的组合代理模型,快速映射参数与振动的关系;最后,以轴承支承刚度和阻尼为优化变量,基于非支配排序遗传算法(Non-dominated Sorting Genetic Algorithm Ⅱ
NSGA-Ⅱ)求解帕累托解集,实现振动加速度最小化目标。
结果
2
优化后系统振动加速度有效值平均降低24%,其中大齿轮垂向振动加速度有效值降幅达38.4%;优化效率提升97%,计算时间从16天缩短至10 min;不同转速下振动抑制效果稳定,且齿轮承载能力未受影响。研究结果可为城轨车辆牵引传动系统优化设计提供参考。
Objective
2
Aiming at the excessive vibration of the traction gear transmission system of urban rail vehicles and the problems of long operation time and slow convergence speed in optimization design
a multi-objective optimization method based on a combined surrogate model was proposed to achieve precise improvement of system vibration characteristics and enhancement of optimization efficiency.
Methods
2
Firstly
a lumped parameter method was used to establish the electromechanical coupling dynamic model of the system
and vibration response data were obtained to provide a foundation for subsequent optimization. Secondly
with structural parameters as inputs and vibration responses as outputs
a combined surrogate model integrating Kriging
radial basis function method (RBF) and polynomial response surface was constructed to quickly map the relation between parameters and vibration. Finally
taking bearing support stiffness and damping as optimization variables
the Pareto solution set was solved based on the non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) to minimize vibration acceleration.
Results
2
After optimization
the root mean square value of the system’s vibration acceleration is reduced by an average of 24%
with the root mean square value of the vertical vibration acceleration of the large gear decreasing by 38.4%; the optimization efficiency is increased by 97%
and the calculation time is shortened from 16 days to 10 min. The vibration suppression effect was stable at different rotational speeds
and the gear bearing capacity is not affected
providing a reference for the optimal design of the traction transmission system of urban rail vehicles.
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