高速列车轴箱轴承载荷反演模型及关键参数优化匹配

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摘要卡尔曼滤波作为状态最优估计算法，可应用于高速列车轴箱轴承载荷反演中，准确建立反演模型的同时，滤波参数的选取也是反演的关键。本文首先推导了转臂轴箱装置的17自由度垂向和横向车辆动力学模型，提出并验证了基于卡尔曼滤波算法的轴承载荷反演方法，分析并确定了模型关键参数的选取，采用自适应小生境遗传算法对其进行多目标多参数优化，最后利用SIMPACK建立一致的车辆动力学模型，计算模拟车辆在施加有轨道随机不平顺的直线上恒速运行，验证反演效果。结果表明：优化了的参数可大幅提升反演效果，验证了轴承载荷反演模型和自适应小生境遗传算法对滤波参数优化方法的正确性，为高速列车轴箱轴承载荷反演及关键参数优化提供方法和经验。

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	唐嘉1
	池茂儒1
	杨晨1
	马子魁2
	姚雪松2
	罗赟1

关键词 ：高速列车, 卡尔曼滤波, 轴承载荷, 遗传算法, 参数优化

Abstract：As an optimal state estimation algorithm, Kalman filter can be applied to the inversion of high-speed train axle box bearing loads. While accurately establishing the inversion model, the selection of filter parameters is also the key to the inversion. In this paper, a 17-degree-of-freedom vertical and lateral vehicle dynamics model of the pivoting arm axle box device is derived, a bearing load inversion method based on the Kalman filter algorithm is proposed and verified, and the selection of key parameters of the model is analyzed and determined. The niche genetic algorithm performs multi-objective and multi-parameter optimization on the key parameters, and finally uses SIMPACK to establish a consistent vehicle dynamics model, calculates and simulates the vehicle running at a constant speed on a straight line with random track irregularities, and verifies the inversion effect. The results show that the optimized parameters can greatly improve the inversion effect, which verifies the correctness of the bearing load inversion model and the adaptive niche genetic algorithm for the filtering parameter optimization method, and provides a basis for the high-speed train axle box bearing load inversion and key parameter setting. Optimization provides methods and experience.

Key words： high-speed train kalman filter bearing load genetic algorithm parameter optimization

收稿日期: 2023-03-27 出版日期: 2024-02-28

引用本文:

唐嘉1，池茂儒1，杨晨1，马子魁2，姚雪松2，罗赟1. 高速列车轴箱轴承载荷反演模型及关键参数优化匹配[J]. 振动与冲击, 2024, 43(4): 52-60.
TANG Jia1，CHI Maoru1，YANG Chen1，MA Zikui2，YAO Xuesong2，LUO Yun1. A load inversion model of the axle box bearing of a high-speed train and optimal match of key parameters. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(4): 52-60.

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http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2024/V43/I4/52

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