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基于恒等映射多层极限学习机的高速列车踏面磨耗预测模型

王美琪 王艺 陈恩利 刘永强 刘鹏飞

王美琪, 王艺, 陈恩利, 刘永强, 刘鹏飞. 基于恒等映射多层极限学习机的高速列车踏面磨耗预测模型. 力学学报, 2022, 54(6): 1720-1731 doi: 10.6052/0459-1879-21-692
引用本文: 王美琪, 王艺, 陈恩利, 刘永强, 刘鹏飞. 基于恒等映射多层极限学习机的高速列车踏面磨耗预测模型. 力学学报, 2022, 54(6): 1720-1731 doi: 10.6052/0459-1879-21-692
Wang Meiqi, Wang Yi, Chen Enli, Liu Yongqiang, Liu Pengfei. High-speed train tread wear prediction model based on I-ML-ELM. Chinese Journal of Theoretical and Applied Mechanics, 2022, 54(6): 1720-1731 doi: 10.6052/0459-1879-21-692
Citation: Wang Meiqi, Wang Yi, Chen Enli, Liu Yongqiang, Liu Pengfei. High-speed train tread wear prediction model based on I-ML-ELM. Chinese Journal of Theoretical and Applied Mechanics, 2022, 54(6): 1720-1731 doi: 10.6052/0459-1879-21-692

基于恒等映射多层极限学习机的高速列车踏面磨耗预测模型

doi: 10.6052/0459-1879-21-692
基金项目: 国家自然科学基金(12102273, 12072205, 52072249)河北省科技计划(20310803 D)资助项目
详细信息
    作者简介:

    陈恩利, 教授, 主要研究方向: 车辆系统动力学、损伤识别与故障诊断. E-mail: chenenl@stdu.edu.cn

  • 中图分类号: TH117.1

HIGH-SPEED TRAIN TREAD WEAR PREDICTION MODEL BASED ON I-ML-ELM

  • 摘要: 针对高速列车车轮踏面磨耗单一模型无法对各种复杂工况下列车车轮踏面磨耗进行定量计算的问题, 提出一种基于恒等映射多层极限学习机的高速列车车轮踏面磨耗测量方法. 首先将恒等映射引入到多层极限学习机中, 提出一种基于恒等映射的多层极限学习机模型(identity multilayer extreme learning machine, I-ML-ELM), 采用机器学习公共数据集对该模型进行性能验证, 数值结果表明I-ML-ELM模型具有较好的准确性与泛化性; 然后基于车辆-轨道耦合动力学理论建立高速列车的车辆-轨道耦合动力学模型, 模拟列车运行的不同工况, 观测和分析高速列车的车轮踏面磨耗情况, 并通过I-ML-ELM预测模型对高速列车车轮踏面磨耗量进行学习及预测; 最后应用高速列车车轮踏面磨耗的实际测量值对I-ML-ELM预测模型进行进一步的验证, 结果表明: I-ML-ELM预测模型的各项性能参数指标在整体上优于以下五种网络: ELM, FLN, ML-ELM, ML-KELM和DLSFLN, 通过高速列车线路实测数据的进一步验证表明, 本文提出的基于I-ML-ELM的高速列车车轮踏面磨耗预测模型能较好地反映不同参数对高速列车车轮踏面磨耗值的影响规律.

     

  • 图  1  ELM-AE网络结构图

    Figure  1.  Network structure diagram of ELM-AE

    图  2  I-ML-ELM网络结构图

    Figure  2.  Network structure diagram of I-ML-ELM

    图  3  不同隐层神经元条件下的网络回归精度变化趋势

    Figure  3.  Variation trend of network regression accuracy under different hidden layer neurons

    图  4  磨耗系数分布图

    Figure  4.  Wear coefficient map

    图  5  列车运行不同里程时踏面廓形及磨耗深度

    Figure  5.  The tread profile and wear depth under different mileage of train operation

    图  6  不同车速下列车运行100000 km 磨耗深度

    Figure  6.  100000 km wear depth of train running at different speeds

    图  7  不同曲线半径下车轮磨耗深度值

    Figure  7.  Wheel wear depth under different curve radius

    图  8  实测列车运行不同里程下车轮踏面廓形图

    Figure  8.  Measured wheel tread profile under different mileage of train operation

    图  9  测试样本的踏面磨耗预测值和实际测量值对比图

    Figure  9.  Comparison between predicted value and sample value of testing sample of wheel tread wear value

    表  1  数据集的基本数据信息

    Table  1.   Basic data information of data set

    Data setAttributesTraining samplesTesting samples
    machine CPU615059
    wine quality1134291469
    California housing8144486192
    estate valuation6290124
    下载: 导出CSV

    表  2  算法在不同网络下数据集RMSE值比较

    Table  2.   Obtained RMSE by different networks of the algorithm

    AlgorithmsMachine CPUWine qualityCalifornia housingEstate valuetion
    ELM0.16160.00190.17910.0011
    FLN0.41850.00190.23080.0011
    DLSFLN127.870.0016195.630.0012
    ML-ELM0.06520.00190.22690.0017
    ML-KELM0.05680.00180.24680.0018
    I-ML-ELM0.03220.00150.13990.0012
    下载: 导出CSV

    表  3  算法在不同网络下数据集MAXE值比较

    Table  3.   Obtained MAXE by different networks of the algorithm

    AlgorithmsMachine CPUWine qualityCalifornia housingEstate valuation
    ELM0.90520.03877.99920.0049
    FLN2.40280.034014.11350.0052
    DLSFLN982.190.00941.53 × 1040.0052
    ML-ELM0.26650.00770.75080.0058
    ML-KELM0.25150.00650.60280.0060
    I-ML-ELM0.12840.00721.26380.0048
    下载: 导出CSV

    表  4  算法在不同网络下数据集MAPE值比较

    Table  4.   Obtained MAPE by different networks of the algorithm

    AlgorithmsMachine CPUWine qualityCalifornia housingEstate valuation
    ELM0.68510.10290.28370.1502
    FLN0.84690.10360.29210.0011
    DLSFLN17.25610.09949.50980.1649
    ML-ELM1.24940.10820.53940.2583
    ML-KELM1.04520.10550.55800.2946
    I-ML-ELM0.56840.09000.28310.1764
    下载: 导出CSV

    表  5  算法在不同网络下数据集MAE值比较

    Table  5.   Obtained MAE by different networks of the algorithm

    AlgorithmsMachine CPUWine qualityCalifornia housingEstate valuation
    ELM0.05860.00130.10908.13 × 10−4
    FLN0.11590.00130.10948.08 × 10−4
    DLSFLN16.70330.00132.58879.13 × 10−4
    ML-ELM0.04500.00140.18131.30 × 10−3
    ML-KELM0.03790.00140.19231.40 × 10−3
    I-ML-ELM0.02340.00110.10239.36 × 10−4
    下载: 导出CSV

    表  6  算法在不同网络下数据集的测试时间(s)比较

    Table  6.   Data test time (s) comparison of the algorithm under different networks

    AlgorithmsMachine CPUWine qualityCalifornia housingEstate valuation
    ML-ELM0.63600.81331.22360.7140
    ML-KELM0.00290.52960.24680.0025
    I-ML-ELM0.00280.00340.00430.0029
    下载: 导出CSV

    表  7  高速列车主要参数

    Table  7.   Basic parameters of high-speed vehicle

    NameSpecification
    car body mass/kg33766
    length between bogie centers/m17.5
    frame mass/kg2280
    wheelset mass/kg1901.8
    running circle diameter/m0.86
    wheelset semibase/m1.493
    下载: 导出CSV

    表  8  不同网络模型在仿真数据下的性能参数

    Table  8.   Performance parameters of different network models under simulation data

    AlgorithmsRMSEMAXEMAPEMAE
    ELM0.09500.2000165.01500.0702
    FLN0.11080.2529279.53500.0826
    DLSFLN0.03550.185020.37850.0158
    ML-ELM0.00210.003412.23210.0019
    ML-KELM0.00180.00600.29460.0014
    I-ML-ELM2.111 × 10−45.5786 × 10−40.47811.6527 × 10−4
    下载: 导出CSV

    表  9  不同模型在现场数据下的性能参数

    Table  9.   Performance parameters of different algorithms under field data

    AlgorithmsRMSEMAXEMAPEMAE
    ELM0.00550.01020.23430.0046
    FLN0.00550.01020.23430.0046
    DLSFLN0.00550.01020.23430.0046
    ML-ELM0.01010.01710.34560.0081
    ML-KELM0.01060.01780.36140.0087
    I-ML-ELM0.00320.00470.15390.0029
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-12-29
  • 录用日期:  2022-04-22
  • 网络出版日期:  2022-04-23
  • 刊出日期:  2022-06-18

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