物理模型与数据融合驱动的动车组车体抖振量化研究

doi:10.3901/JME.260733

摘要/Abstract

摘要： 动车组车体抖振由转向架蛇行与车体菱形模态耦合引发，严重影响运行平稳性，抖振状态实时监测对于行车安全至关重要。针对传统方法难以精准区分非抖振、孕育、抖振阶段，提出物理模型与数据融合的抖振量化方法。物理层面，通过多源测试分析，揭示转向架同相位蛇行阻尼比降低与模态频率差减小引发耦合振动并诱发抖振；数据层面，采用改进变分模态分解(Variational mode decomposition，VMD)和希尔伯特变换(Hilbert transform，HT)提取振动瞬时特征，构建模态聚能指纹(R_E)、模态阻尼指纹(D_b)、模态共锁系数(L_m)、模态相干系数(P_fr)四维核心指标刻画抖振演化链，融合500组数据(仿真200组、实测200组、增强100组)，采用核密度估计(Kernel density estimation，KDE)建模三阶段特征分布，结合贝叶斯决策与“持续时间多证据一致性门控”，降低误警。试验表明，所提指标可有效区分抖振三阶段，模型判定与实际工况吻合度高，为车体抖振在线监测与抑制提供量化基础。

关键词: 列车, 模态, 数据融合, 模态特征指标, 指标量化

Abstract: The carbody chattering of EMUs is caused by the coupling of bogie hunting and carbody diamond-shape mode, which seriously affects running stability. Real-time monitoring of the chattering status is crucial for driving safety. Given that traditional methods struggle to accurately distinguish the"non-chattering, incubation, and chattering" stages, a chattering quantification method integrating physical models and data is proposed. At the physical level, through multi-source test analysis, it is revealed that the reduced damping ratio of bogie in-phase hunting and the decreased modal frequency difference induce coupled vibration, thereby triggering chattering. At the data level, improved variational mode decomposition(VMD) and Hilbert transform(HT) are adopted to extract instantaneous vibration features. Four-dimensional core indicators are constructed to characterize the chattering evolution chain，including modal energy-gathering fingerprintR_E, modal damping fingerprint D_b, modal interlocking coefficient L_m, and modal coherence coefficient P_fr. By fusing 500 sets of data (200 sets of simulation data, 200 sets of measured data, and 100 sets of augmented data), kernel density estimation(KDE) is used to model the feature distributions of the three stages. Combined with Bayesian decision-making and the “duration gate plus multi-evidence consistency gate”, false alarms are reduced. Experiments show that the proposed indicators can effectively distinguish the three stages of chattering, and the model's judgment is highly consistent with actual working conditions, providing a quantitative basis for online monitoring and suppression of carbody chattering.

Key words: train, modal, data fusion, modal characteristic index, index quantification

中图分类号:

U270

邓新, 宫岛, 周凯, 周劲松, 王瑞乾, 孙维光. 物理模型与数据融合驱动的动车组车体抖振量化研究[J]. 机械工程学报, 2026, 62(16): 29-40.

DENG Xin, GONG Dao, ZHOU Kai, ZHOU Jinsong, WANG Ruiqian, SUN Weiguang. Quantitative Research on Shimmy of EMU Car Body Driven by Physical Model and Data Fusion[J]. Journal of Mechanical Engineering, 2026, 62(16): 29-40.

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链接本文: http://www.cjmenet.com.cn/CN/10.3901/JME.260733

http://www.cjmenet.com.cn/CN/Y2026/V62/I16/29

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