基于Kriging模型和无迹卡尔曼滤波的转向架构架模型修正

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振动与冲击 ›› 2022, Vol. 41 ›› Issue (4) : 270-277.

论文

赵敏龙，彭珍瑞，张亚峰

作者信息 +

Model updating of a bogie frame based on the Kriging model and the unscented Kalman filter

ZHAO Minlong, PENG Zhenrui, ZHANG Yafeng

Author information +

文章历史 +

摘要

为提高转向架构架模型的修正效率和实时性，提出了一种基于Kriging模型和无迹卡尔曼滤波的模型修正方法。首先，对构架进行模态分析，引入信息熵确定模态阶数来优选频响函数频率区间。其次，构造Kriging模型，将频响函数经过小波变换并提取第四层低频系数作为Kriging模型输出，并通过改进的灰狼算法(grey wolf optimizer，GWO)确定Kriging模型相关参数值。最后，以待修正参数作为状态向量，以Kriging模型预测的小波系数和真实响应的小波系数之差的平方和作为观测函数，通过无迹卡尔曼滤波算法求解待修正参数。结果表明，所提方法对构架模型参数修正有良好的精度、效率和鲁棒性，且在0.03秒内收敛到真实值。

Abstract

In order to improve the efficiency and real-time performance of bogie frame model updating, a model updating method for based on Kriging model and unscented Kalman filter is proposed. Firstly, carry out modal analysis on the frame, introduce information entropy to determine the modal order to optimize the frequency range of the frequency response function. Secondly, construct the Kriging model, and extract the frequency response function through wavelet transform,and the low-frequency coefficients of the fourth layer are the outputs of the Kriging model, and the relevant parameter values of the Kriging model are determined by the improved gray wolf algorithm. Finally, the parameters to be updated are used as the state vector, and take the square sum of the difference between the wavelet coefficients predicted by the Kriging model and the wavelet coefficients of the real response as the observation function, and the parameters to be updated are solved by the unscented Kalman filter. The results show that the proposed method has good accuracy, efficiency and robustness, and that the true value is converged within 0.03 seconds.

导出引用

赵敏龙，彭珍瑞，张亚峰. 基于Kriging模型和无迹卡尔曼滤波的转向架构架模型修正[J]. 振动与冲击, 2022, 41(4): 270-277

ZHAO Minlong, PENG Zhenrui, ZHANG Yafeng. Model updating of a bogie frame based on the Kriging model and the unscented Kalman filter[J]. Journal of Vibration and Shock, 2022, 41(4): 270-277

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