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在轨道车辆行驶过程中，车轮易形成多边形磨耗，这会对列车行驶的安全性与舒适性产生负面影响。针对此问题，文章提出了一种基于轨道振动信号的车轮多边形特征提取算法，旨在通过采集钢轨振动信号，挖掘并识别车轮失圆状态的特征信息。首先，基于多体动力学理论与动力学建模平台，构建了刚柔耦合的车轮-轨道系统动力学模型；其次，从该模型中提取了钢轨振动加速度信号，进而提出了时域特征提取的多层次分析方法，来判断正常车轮与非正常车轮；再次，采用小波变换对时域信号进行了分解，并引入Mallat算法完成了5次变换，得到了5个逼近系数后计算样本熵；最后，通过对比不同工况下的时域指标和样本熵差别来进行车轮故障诊断，并通过了实测数据的验证。该研究成果为车轮失圆在线检测提供了新的技术手段，对提升列车运行安全性、降低维护成本具有重要的工程应用价值。
Abstract：During the operation of rail vehicles, wheels are prone to developing polygonal wear, which negatively impacts the safety and comfort of trains. To address this issue, this paper proposes a feature extraction algorithm for wheel polygon based on track vibration signals. By collecting vibration signals from the rails, the method aims to uncover and identify characteristic information related to wheel out-of-round conditions. Firstly, a rigid-flexible coupled wheel-rail system dynamics model was constructed based on multibody dynamics theory and a dynamic modeling platform. Secondly, rail vibration acceleration signals were extracted from this model, and a multi-level analysis method for time-domain feature extraction was proposed to distinguish between normal and abnormal wheels. Thirdly, wavelet transform was applied to decompose the time-domain signals, and the Mallat algorithm was introduced to perform five levels of decomposition. After obtaining five sets of approximation coefficients, the sample entropy was calculated. Finally, wheel fault diagnosis was conducted by comparing differences in time-domain indicators and sample entropy under various operating conditions, and the method was validated using measured data. This research outcome provides a new technical approach for online detection of wheel out-of-round, offering significant engineering application value for enhancing train operational safety and reducing maintenance costs.
[1] 吴越.高速列车车轮多边形磨耗机理及其影响研究[D].成都：西南交通大学，2023.
[2] 陈光雄，金学松，邬平波，等.车轮多边形磨耗机理的有限元研究[J].铁道学报，2011,33(1):14-18.CHEN Guangxiong,JIN Xuesong,WU Pingbo,et al.Finite element study on the generation mechanism of polygonal wear of railway wheels[J].Journal of the China Railway Society,2011,33 (1):14-18.
[3] 董小乐.轨道交通车轮多边形成因研究[D].西安：西安工业大学，2023.
[4] 陈迪来，曾毅，夏张辉，等.铁道车辆不同等效锥度计算方法对比及软件编制[J].应用技术学报，2023,23(4):376-384.CHEN Dilai,ZENG Yi,XIA Zhanghui,et al.Comparison of different equivalent conicity calculation methods and software programming of railway vehicles[J].Journal of Technology,2023,23(4):376-384.
[5] 徐晓迪，刘金朝，孙善超，等.基于车辆动态响应的车轮多边形自动识别方法[J].铁道建筑，2019,59(9):101-105.XU Xiaodi,LIU Jinzhao,SUN Shanchao,et al.Automatic method for polygonalization of wheel treads based on vehicle dynamic response [J].Railway Engineering,2019,59 (9):101-105.
[6] 许文天，梁树林，池茂儒，等.基于频响函数的车轮多边形磨耗车载定量诊断方法[J].中国机械工程，2025(5):942-953.XU Wentian,LIANG Shulin,CHI Maoru,et al.Wheel polygonal wear on-board quantitative diagnostic method based on frequency response function [J].China Mechanical Engineering,2025(5):942-953.
[7] 王秋实，王泽根，周劲松，等.基于迭代修正DFT的车轮多边形磨耗状态识别[J].振动，测试与诊断，2023,43(3):485-492.WANG Qiushi,WANG Zegen,ZHOU Jinsong,et al.Detection framework of wheel polygon wear state based on iterative modified DFT[J].Journal of Vibration,Measurement & Diagnosis,2023,43 (3):485-492.
[8] 黄振鑫.高速列车高阶车轮多边形在途检测方法[D].成都：西南交通大学，2023.
[9] 廖小康.复杂激扰下列车轴箱系统振动特性仿真及复合故障特征提取研究[D].成都：西南交通大学，2022.
[10] 张超，秦敏敏，张少飞.改进MCKD-MEEMD在滚动轴承故障诊断中的应用[J].机械设计与制造，2024(7):193-199.ZHANG Chao,QIN Minmin,ZHANG Shaofei.Application of improved MCKD-MEEMD in fault diagnosis of rolling bearings[J].Machinery Design & Manufacture,2024(7):193-199.
[11] 陈博.高速列车车轮多边形的检测与识别方法研究[D].成都：西南交通大学，2018.
[12] 胡林桥，王兴宇.基于MEEMD-SVM的城轨车辆车轮多边形故障诊断方法[J].科技与创新，2020(19):1-3.HU Linqiao,WANG Xingyu.Fault diagnosis method of wheel polygon of urban rail vehicle based on MEEMD-SVM[J].Technology and Innovation,2020(19):1-3.
[13] 邓磊鑫.基于一维卷积神经网络的高速列车车轮失圆类型识别方法研究[D].成都：西南交通大学，2023.
[14] 李大柱.基于时频图与卷积神经网络的车轮失圆智能识别[D].成都：西南交通大学，2023.
[15] SONG Y,SUN B.Recognition of wheel polygon based on W/R force measurement by piezoelectric sensors in GSM-R network[J].Wireless Personal Communications,2018,102(2):1283-1291.
[16] 罗佳文.基于轴箱振动加速度的车轮多边形磨耗状态监测方法研究[D].南昌：华东交通大学，2021.
[17] 黄琴.高速列车车轮多边形限值研究[D].南昌：华东交通大学，2021.
[18] 吴磊，钟硕乔，金学松，等.车轮多边形化对车辆运行安全性能的影响[J].交通运输工程学报，2011(3):47-54.WU Lei,ZHONG Shuoqiao,JIN Xuesong,et al.Influence of polygonal wheel on running safety of vehicle[J].Journal of Traffic and Transportation Engineering,2011 (3):47-54.
[19] 何正嘉.现代信号处理及工程应用[M].西安：西安交通大学出版社，2007.
[20] PENG Z K,CHU F L.Application of the wavelet transform in machine condition monitoring and fault diagnostics:A review with bibliography[J].Mechanical Systems and Signal Processing,2004 (18):199-221.
[21] BELOTTI V,CRENNA F,MICHELINI R C,et al.Time evolutionary analysis:Operator driven paradigms for fault diagnosis[C]//3rd IEEE International Symposium on Diagnostics for Electrical Machines,Power Electronics and Drives.2001:1-3.
[22]赵磊，夏均忠，李泽华，等．基于VMD样本熵和LS-SVM的滚动轴承故障诊断[J]．军事交通学院学报，2017,19(4):43-47.ZHAO Lei,XIA Junzhong,LI Zehua,et al.Fault diagnosis of rolling bearing based on VMD sample entropyand LS-SVM[J].Journal of MilitaryTransportation University，2017,19(4):43-47.
基本信息:
DOI：10.20213/j.cnki.tdcl.2025.06.001
中图分类号:U279
引用信息:
[1]陈迪来,丁振,吴剑豪,等.车轮多边形特征提取算法研究[J].铁道车辆,2025,63(06):27-34+55.DOI:10.20213/j.cnki.tdcl.2025.06.001.
基金信息:
上海市“一带一路”中老铁路工程国际联合实验室项目(21210750300); 上海应用技术大学协同创新基金(XTCX2024-07)
2025-12-20
2025-12-20