基于Hilbert-Huang变换的高速铁路轨道短波不平顺识别研究

doi:10.3901/JME.2025.20.274

机械工程学报 ›› 2025, Vol. 61 ›› Issue (20): 274-282.doi: 10.3901/JME.2025.20.274

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基于Hilbert-Huang变换的高速铁路轨道短波不平顺识别研究

周素霞^1,2, 辛欣^1,2, 曲直^1,3

1. 北京建筑大学机电与车辆工程学院北京 100044;
2. 北京建筑大学城市轨道交通车辆服役性能保障北京市重点实验室北京 100044;
3. 内蒙古第一机械集团有限公司包头 014030

收稿日期:2024-11-07 修回日期:2025-05-11 发布日期:2025-12-03
作者简介:周素霞，女，1971年出生，博士，教授，硕士研究生导师。主要研究方向为结构的疲劳与断裂、金属材料的微观损伤、车辆系统动力学。E-mail:sxzhou1971@163.com
辛欣（通信作者），女，1993年出生。博士、硕士研究生导师。主要研究方向为轨道-车辆系统动力学。E-mail:xinxin@bucea.edu.cn
曲直，男，1995年出生。主要研究方向为结构的疲劳与断裂、车辆系统动力学。E-mail:396828028@qq.com
基金资助:
北京市自然科学基金资助项目(L211007)。

Research on Recognition of Shortwave Irregularity of High Speed Railway Track Based on Hilbert-Huang Transform

ZHOU Suxia^1,2, XIN Xin^1,2, QU Zhi^1,3

1. School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing, 100044;
2. Beijing Key Laboratory of Service Performance of Vehicles, Beijing University of Civil Engineering and Architecture, Beijing 100044;
3. Inner Mongolia First Machinery Group Co., LTD., Baotou 014030

Received:2024-11-07 Revised:2025-05-11 Published:2025-12-03

摘要/Abstract

摘要： 高速列车运行时，短波不平顺是产生车辆噪声和改变轮轨相互作用力的主要原因之一。轴箱加速度可以反映短波不平顺引起的高频振动响应。结合轴箱加速度，提出基于Hilbert-Huang变换的高速铁路轨道短波不平顺识别方法。构建高频轮轨接触仿真模型，通过Matlab程序对仿真模型施加周期性短波不平顺，获得其动力学响应。对仿真模型轴箱加速度信号进行经验模态分解(Empirical mode decomposition, EMD)并结合边际谱分析确定符合不平顺特征频率的本征模态函数(Intrinsic mode function，IMF)分量，对IMF分量进行Hilbert谱分析获取不平顺位置和波长信息。通过实测轴箱加速度数据对该方法进行验证，将识别结果与现场路段轨道检测的实际情况进行对比。结果表明，高频轮轨接触仿真模型能够有效地输出周期性短波不平顺条件下的动力学响应。基于Hilbert-Huang变换的高速铁路轨道周期性短波不平顺识别方法，能够有效地获取短波不平顺位置和波长信息，识别出的短波不平顺位置和波长与实测情况相符，对周期性短波不平顺的识别具有一定指导意义。

关键词: Hilbert-Huang变换, 经验模态分解, 短波不平顺, 时频分析, 轴箱加速度

Abstract: When the high-speed train is running, the short-wave irregularity is one of the main reasons to produce vehicle noise and change the wheel-rail interaction force. The high frequency vibration response can be reflected on the axle box acceleration caused by short-wave irregularity. Combined with axle box acceleration, a short-wave irregularity identification method of high-speed railway track based on Hilbert-Huang transform is proposed. The periodic shortwave irregularity is applied to the simulation model by Matlab program, and its dynamic response is obtained. Empirical mode decomposition of axle box acceleration signal of simulation model and the intrinsic mode function (IMF) which accords with the characteristic frequency of irregularity is determined by marginal spectrum analysis. The IMF components is analyzed by Hilbert spectrum to obtain the irregularity position and wavelength information. The method is verified by the measured axle box acceleration data, and the identification results are compared with the actual situation of track detection on the road section. The results show that the dynamic response can be effectively output by the high frequency wheel-rail contact simulation model under the condition of periodic shortwave irregularity. The position and wavelength information of shortwave irregularity can be effectively obtained by the recognition method of periodic shortwave irregularity of high-speed railway track based on Hilbert-Huang transform, and the identified position and wavelength of shortwave irregularity are consistent with the measured results, which has a certain guiding significance for the identification of periodic shortwave irregularity.

Key words: Hilbert-Huang transform, empirical mode decomposition, shortwave irregularity, time-frequency analysis, axle box acceleration

中图分类号:

U270

周素霞, 辛欣, 曲直. 基于Hilbert-Huang变换的高速铁路轨道短波不平顺识别研究[J]. 机械工程学报, 2025, 61(20): 274-282.

ZHOU Suxia, XIN Xin, QU Zhi. Research on Recognition of Shortwave Irregularity of High Speed Railway Track Based on Hilbert-Huang Transform[J]. Journal of Mechanical Engineering, 2025, 61(20): 274-282.

参考文献

[1] 殷华，朱洪涛，王志勇，等. 基于多弦模型的轨道短波不平顺测量研究[J]. 振动与冲击，2017，36(14)：178- 182，193. YIN Hua，ZHU Hongtao，WANG Zhiyong，et al. Research on track short-wave irregularity measurement based on multi-string model[J]. Vibration and Shock，2017，36(14)：178-182，193.
[2] 刘金朝，徐晓迪，牛留斌，等. 高速铁路轨道短波健康状态动态诊断模型和方法[J]. 铁道学报，2021，43(10)：69-74. LIU Jinchao，XU Xiaodi，NIU Liubin，et al. Dynamic diagnosis model and method of short wave health state of high-speed railway track[J]. Journal of Railway，2021，43(10)：69-74.
[3] 刘金朝，陈东生，赵钢，等. 评判高铁轨道短波不平顺病害的轨道冲击指数法[J]. 中国铁道科学，2016，37(4)：34-41. LIU Jinchao，CHEN Dongsheng，ZHAO Gang，et al. Track impact Index method for judging short wave irregularity of high speed railway track[J]. China Railway Science，2016，37(4)：34-41.
[4] MOLODOVA M，LI Z，NUNEZ A，et al. Monitoring the railway infrastructure：Detection of surface defects using wavelets[C/CD]//International IEEE Conference on Intelligent Transportation Systems. IEEE，2013.
[5] LIU N，GAO J，JIANG X，et al. Seismic time–frequency analysis via STFT-based concentration of frequency and time[J]. IEEE Geoscience and Remote Sensing Letters，2017，14(1)：127-131.
[6] HUANG N E，SHEN Z，LONG S R，et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings Mathematical Physical & Engineering Sciences，1998，454(1971)：903-995.
[7] 毛炜，金荣洪，耿军平，等. 一种基于改进Hilbert- Huang变换的非平稳信号时频分析法及其应用[J]. 上海交通大学学报，2006(5)：724-729. MAO Wei，JIN Ronghong，GENG Junping，et al. A non- stationary signal time-frequency analysis method based on improved Hilbert-Huang transform and its application[J]. Journal of Shanghai Jiaotong University，2006(5)：724-729.
[8] 崔永谦，刘延利，王孟华，等. 利用地震资料Hilbert- Huang变换划分沉积旋回[J]. 石油地球物理勘探，2016，51(5)：983-989，1001，838. CUI Yongqian，LIU Yanli，WANG Menghua，et al. Hilbert-Huang transform of seismic data is used to divide sedimentary cycles[J]. Petroleum Geophysical Exploration，2016，51(5)：983-989，1001，838.
[9] SONG D.，LIU X，HUANG J，et al. Energy-based analysis of seismic failure mechanism of a rock slope with discontinuities using Hilbert-Huang transform and marginal spectrum in the time-frequency domain[J]. Landslides，2021，18：105-123.
[10] CHERIF B D E，BENDIABDELLAH A，TABBAKH M. An automatic diagnosis of an inverter IGBT open-circuit fault based on HHT-ANN[J]. Electric Power Components and Systems，2020(2)：1-14.
[11] 周小龙，刘薇娜，姜振海，等. 改进的HHT方法及其在旋转机械故障诊断中的应用[J]. 振动与冲击，2020，39(7)：189-195. ZHOU Xiaolong，LIU Weina，JIANG Zhenhai，et al. Improved HHT method and its application in fault diagnosis of rotating machinery[J]. Vibration and Shock，2020，39(7)：189-195.
[12] DIAO Y，JIA D，LIU G，et al. Structural damage identification using modified Hilbert–Huang transform and support vector machine[J]. Journal of Civil Structural Health Monitoring，2021，11(4)：1155-1174.
[13] 张义平. 爆破震动信号的HHT分析与应用研究[D]. 长沙：中南大学，2006. ZHANG Yiping. HHT analysis and application of blasting vibration signal[D]. Changsha：Central South University，2006.
[14] 牛留斌，刘金朝. 基于高频轮轨接触模型的轨道短波不平顺敏感波长特性分析[J]. 机械工程学报，2019，55(8)：173-182. NIU Liubin，LIU Jinchao. Analysis of sensitive wavelength characteristics of track shortwave irregularity based on high frequency wheel-rail contact model[J]. Journal of Mechanical Engineering，2019，55(8)：173-182.
[15] STEENBERGEN M. Quantification of dynamic wheel- rail contact forces at short rail irregularities and application to measured rail welds[J]. Journal of Sound & Vibration，2008，312(4-5)：606-629.
[16] MOLODOVA M，LI Z，DOLLEVOET R. Axle box acceleration：Measurement and simulation for detection of short track defects[J]. Wear，2011，271(1–2)：349-356.
[17] 牛留斌，李谷，刘金朝，等. 轮轨力在轨道短波不平顺检测中的应用[J]. 铁道建筑，2019，59(8)：133-139. NIU Liubin，LI Gu，LIU Jinchao，et al. Application of wheel-rail force in track short-wave irregularity detection[J]. Railway Construction，2019，59(8)：133-139.
[18] 于淼. 高速铁路轨道-车辆系统高频瞬态仿真及波磨机理研究[D]. 北京：中国铁道科学研究院，2019. YU Miao. High-frequency transient simulation and wave grinding mechanism of high-speed railway track-vehicle system[D]. Beijing：China Academy of Railway Sciences，2019.

基于Hilbert-Huang变换的高速铁路轨道短波不平顺识别研究

Research on Recognition of Shortwave Irregularity of High Speed Railway Track Based on Hilbert-Huang Transform

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