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基于优化多核极限学习机的车轮多边形磨耗识别

谢博 陈是扦 徐明坤 杨云帆 王开云

谢博, 陈是扦, 徐明坤, 杨云帆, 王开云. 基于优化多核极限学习机的车轮多边形磨耗识别. 力学学报, 待出版 doi: 10.6052/0459-1879-22-083
引用本文: 谢博, 陈是扦, 徐明坤, 杨云帆, 王开云. 基于优化多核极限学习机的车轮多边形磨耗识别. 力学学报, 待出版 doi: 10.6052/0459-1879-22-083
Xie Bo, Chen Shiqian, Xu Mingkun, Yang Yunfan, Wang Kaiyun. Polygonal wear identification of wheels based on optimized multiple kernel extreme learning machine. Chinese Journal of Theoretical and Applied Mechanics, in press doi: 10.6052/0459-1879-22-083
Citation: Xie Bo, Chen Shiqian, Xu Mingkun, Yang Yunfan, Wang Kaiyun. Polygonal wear identification of wheels based on optimized multiple kernel extreme learning machine. Chinese Journal of Theoretical and Applied Mechanics, in press doi: 10.6052/0459-1879-22-083

基于优化多核极限学习机的车轮多边形磨耗识别

doi: 10.6052/0459-1879-22-083
基金项目: 国家自然科学基金项目 (52005416, 51825504, U19 A20110) 和四川省科技计划项目 (2020 YJ0213) 资助
详细信息
    作者简介:

    王开云, 研究员, 主要研究方向:重载铁路工程动力学理论与运营安全技术. E-mail: kywang@swjtu. edu. cn

  • 中图分类号: U211.5;TP181

POLYGONAL WEAR IDENTIFICATION OF WHEELS BASED ON OPTIMIZED MULTIPLE KERNEL EXTREME LEARNING MACHINE

  • 摘要: 多边形车轮是铁路机车车辆中普遍存在的一种磨损现象, 随着列车运营里程的增加, 车轮磨耗程度显著提升, 严重影响着列车乘坐舒适性和运营安全性, 借助于列车运营监测大数据开展多边形车轮动态检测方法研究具有重要意义. 本研究基于列车轴箱垂向加速度建立了多边形车轮定量识别模型, 首先通过阶次分析识别出轴箱加速度中包含的多边形车轮主要阶次, 同时获取各阶次对应的加速度幅值信息, 在此基础上引入加速度信号熵特征共同构建多边形车轮磨耗幅值识别特征矩阵, 然后建立遗传变异粒子群优化多核极限学习机 (GMPSO-MELM) 识别模型, 通过特征矩阵与磨耗幅值的映射关系, 进一步实现了车轮多边形磨耗幅值识别. 通过仿真与现场实测数据研究结果表明, 所提出的识别模型能有效地从轴箱加速度中提取多边形车轮主要阶次, 磨耗幅值的识别精度均优于对比模型且具有较高的检测效率, 可实现均方根误差为0.0010 (仿真结果) 与0.0134 (试验结果) 的精确识别, 本文提出的多边形车轮磨耗识别模型可为列车车轮检测与智能维护提供理论基础.

     

  • 图  1  熵特征计算流程图

    Figure  1.  Calculation flow chart of entropy characteristics

    图  2  多边形车轮识别算法流程图

    Figure  2.  Flow chart of polygonal wheels identification algorithm

    图  3  机车动力学模型

    Figure  3.  Locomotive dynamic model

    图  4  仿真多边形车轮

    Figure  4.  Simulated polygonal wheels

    图  5  轴箱垂向加速度时域信号

    Figure  5.  Time-domain vertical acceleration signals of axle box

    图  6  多边形车轮阶次识别结果

    Figure  6.  The order identification results of polygonal wheels

    图  7  参数组合寻优迭代过程

    Figure  7.  Iterative process of parameter combination optimization

    图  8  多边形车轮磨耗幅值识别结果

    Figure  8.  The wear amplitude identification results of polygonal wheels

    图  9  不同训练集样本数量的识别误差

    Figure  9.  Identification errors of the different number of training samples

    图  10  现场试验. (a)试验机车. (b)轴箱加速度测量.(c)车轮镟修. (d)多边形车轮测量.

    Figure  10.  Field testing (a) Testing locomotive. (b) Measurement of axle box acceleration. (c) Wheel lathing. (d) Measurement of polygonal wheels

    图  11  试验多边形车轮阶次与磨耗幅值测试结果

    Figure  11.  The order and wear amplitude testing results of polygonal wheels

    图  12  实测轴箱垂向加速度信号

    Figure  12.  Field test vertical acceleration signals of axle box

    图  13  多边形车轮阶次识别结果

    Figure  13.  The order identification results of polygonal wheels

    图  14  多边形车轮磨耗幅值识别结果

    Figure  14.  The wear amplitude identification results of polygonal wheels

    表  1  不同模型的磨耗幅值识别RMSE与时间

    Table  1.   Identification RMSE and time of wear amplitude with different models

    ModelsTest 1Test 2Test 3Time/s
    ELM0.02590.02430.02660.0001
    KELM
    (RBF Kernel)
    0.01070.01640.00910.0094
    KELM
    (Polynomial Kernel)
    0.01380.01490.01620.0029
    KELM
    (Wavelet Kernel)
    0.00370.01360.00480.0089
    GMPSO−MKELM0.00180.00370.00100.0173
    下载: 导出CSV

    表  2  不同模型的磨耗幅值识别RMSE与时间

    Table  2.   Identification RMSE and time of wear amplitude with different models

    ModelsTest 1Test 2Test 3Time/s
    ELM0.02420.02550.02680.0001
    KELM
    (RBF Kernel)
    0.02250.02000.02350.0008
    KELM
    (Polynomial Kernel)
    0.02170.02310.02470.0003
    KELM
    (Wavelet Kernel)
    0.02100.01960.02020.0007
    GMPSO-MKELM0.01830.01340.01650.0018
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-02-25
  • 录用日期:  2022-05-10
  • 网络出版日期:  2022-05-09

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