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中文核心期刊
Volume 54 Issue 6
May  2022
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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

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

doi: 10.6052/0459-1879-21-692
  • Received Date: 2021-12-29
  • Accepted Date: 2022-04-22
  • Available Online: 2022-04-23
  • Publish Date: 2022-06-18
  • For purpose of solving the problem that a single model of high-speed train wheel tread wear cannot be used for quantitative calculation of train wheel tread wear under various complicated working conditions, we propose a feasible measurement method of wheel tread wear of high-speed trains based on the multilayer extreme learning machine with identity mapping. Firstly, we introduce the identity mapping into the multilayer extreme learning machine, then we propose an identity multilayer extreme learning machine (I-ML-ELM) model. In order to test the I-ML-ELM validity, it is applied to four multivariate regression data sets which are from machine learning public data sets. And experimental results show that the I-ML-ELM can achieve a perfect generalization performance and stability at a fast training speed and a quick reaction of the trained network to new observations. Secondly, based on the vehicle-track coupled dynamics theory, we establish the vehicle-track coupling dynamics model of high-speed trains. By simulating different working conditions, we observe the wheel tread wear of high-speed trains, and the I-ML-ELM prediction model is used to learn and predict the wheel tread wear of high-speed trains. Finally, in order to further test the effectiveness of I-ML-ELM prediction model, it is applied to the actual measurement value of wheel tread wear of high-speed train. The results show that compared with those five learning machines (extreme learning machine, fast learning machine, multilayer extreme learning machine, multilayer kernel extreme learning machine and derived least square fast learning network), the performance parameters of I-ML-ELM prediction model are the best as a whole and the model achieves a very good prediction precision and generalization ability. The further verification of the measured data of high-speed train lines show that the prediction model based on I-ML-ELM can not only reflect the influence of different operating parameters on the wheel tread wear value of high-speed trains better, but also realize the prediction of wheel tread wear.

     

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  • [1]
    丁叁叁, 陈大伟, 刘加利. 中国高速列车研发与展望. 力学学报, 2021, 53(1): 35-50 (Ding Sansan, Chen Dawei, Liu Jiali. Research, development and prospect of China high-speed train. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(1): 35-50 (in Chinese)

    Ding Sansan, Chen Dawei, Liu Jiali. Research, development and prospect of China high-speed train. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(1): 35-50 (in Chinese)
    [2]
    许磊, 程道来, 许聪等. 350 km/h动车组车轮磨耗对动力学影响研究. 科学技术与工程, 2020, 20(17): 7046-7051 (Xu Lei, Cheng Daolai, Xu Cong, et al. Effect of wheel wear on dynamics performance of 350 km/h EMU. Science Technology and Engineering, 2020, 20(17): 7046-7051 (in Chinese) doi: 10.3969/j.issn.1671-1815.2020.17.050

    Xu Lei, Cheng Daolai, Xu Cong, et al. Effect of wheel wear on dynamics performance of 350 km/h EMU. Science Technology and Engineering, 2020, 20(17): 7046-7051 (in Chinese) doi: 10.3969/j.issn.1671-1815.2020.17.050
    [3]
    曾元辰, 张卫华, 宋冬利. 高速列车踏面凹形磨耗及其动力学影响规律. 铁道机车车辆, 2018, 38(4): 5-9 (Zeng Yuanchen, Zhang Weihua, Song Dongli. Wheel profile concave wear and its effect law on vehicle dynamics of high-speed trains. Railway Locomotive & Car, 2018, 38(4): 5-9 (in Chinese) doi: 10.3969/j.issn.1008-7842.2018.04.02

    Zeng Yuanchen, Zhang Weihua, Song Dongli. Wheel profile concave wear and its effect law on vehicle dynamics of high-speed trains. Railway Locomotive & Car, 2018, 38(4): 5-9 (in Chinese) doi: 10.3969/j.issn.1008-7842.2018.04.02
    [4]
    Archard JF. Contact and rubbing of flat Surfaces. Journal of Applied Physics, 1953, 24(8): 981-988 doi: 10.1063/1.1721448
    [5]
    Chudzikiewicz A. Modeling of wheel and rail wear. The Archives of Transport, 2001, 13: 5-24
    [6]
    Krause H, Poll G. Wear of wheel-rail surfaces. Wear, 1986, 113(1): 103-122 doi: 10.1016/0043-1648(86)90060-8
    [7]
    Mcewen IJ, Harvey RF. Full-scale wheel-on-rail wear testing: Comparisons with service wear and a developing theoretical predictive method. Lubrication Engineering, 1985, 41(2): 80-88
    [8]
    陶功权, 李霞, 邓永果等. 基于车辆横向运动稳定性的车轮磨耗寿命预测. 机械工程学报, 2013, 49(10): 28-34 (Tao Gongquan, Li Xia, Deng Yongguo, et al. Wheel wear life prediction based on lateral motion stability of vehicle system. Journal of Mechanical Engineering, 2013, 49(10): 28-34 (in Chinese) doi: 10.3901/JME.2013.10.028

    Tao Gongquan, Li Xia, Deng Yongguo, et al. Wheel wear life prediction based on lateral motion stability of vehicle system. Journal of Mechanical Engineering, 2013, 49(10): 28-34 (in Chinese) doi: 10.3901/JME.2013.10.028
    [9]
    吴娜, 曾京. 高速车辆轮轨接触几何关系及车轮磨耗疲劳研究. 中国铁道科学, 2014, 35(4): 80-87 (Wu Na, Zeng Jing. Investigation into wheel-rail contact geometry relationship and wheel wear fatigue of high-speed vehicle. China Railway Science, 2014, 35(4): 80-87 (in Chinese) doi: 10.3969/j.issn.1001-4632.2014.04.12

    Wu Na, Zeng Jing. Investigation into wheel-rail contact geometry relationship and wheel wear fatigue of high-speed vehicle. China Railway Science, 2014, 35(4): 80-87 (in Chinese) doi: 10.3969/j.issn.1001-4632.2014.04.12
    [10]
    孙丽霞, 李晓峰, 胡晓依等. 高速动车组车轮磨耗对轮轨接触关系及车辆动力学性能的影响. 中国铁道科学, 2020, 41(6): 117-126 (Sun Lixia, Li Xiaofeng, Hu Xiaoyi, et al. Influence of wheel wear on wheel-rail contact relationship and vehicle dynamic performance of high-speed EMU. China Railway Science, 2020, 41(6): 117-126 (in Chinese) doi: 10.3969/j.issn.1001-4632.2020.06.13

    Sun Lixia, Li Xiaofeng, Hu Xiaoyi, et al. Influence of wheel wear on wheel-rail contact relationship and vehicle dynamic performance of high-speed EMU. China Railway Science, 2020, 41(6): 117-126 (in Chinese) doi: 10.3969/j.issn.1001-4632.2020.06.13
    [11]
    谢晨月, 袁泽龙, 王建春等. 基于人工神经网络的湍流大涡模拟方法. 力学学报, 2021, 53(1): 1-16 (Xie Chenyue, Yuan Zelong, Wang Jianchun, et al. Artificial neural network-based subgrid-scale models for large-eddy simulation of turbulence. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(1): 1-16 (in Chinese)

    Xie Chenyue, Yuan Zelong, Wang Jianchun, et al. Artificial neural network-based subgrid-scale models for large-eddy simulation of turbulence. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(1): 1-16 (in Chinese)
    [12]
    汪运鹏, 杨瑞鑫, 聂少军等. 基于深度学习技术的激波风洞智能测力系统研究. 力学学报, 2020, 52(5): 1304-1313 (Wang Yunpeng, Yang Ruixin, Nie Shaojun, et al. Deep-learning-based intelligent force measurement system using in a shock tunnel. Chinese Journal of Theoretical and Applied Mechanics, 2020, 52(5): 1304-1313 (in Chinese)

    Wang Yunpeng, Yang Ruixin, Nie Shaojun, et al. Deep-learning-based intelligent force measurement system using in a shock tunnel. Chinese Journal of Theoretical and Applied Mechanics, 2020, 52(5): 1304-1313 (in Chinese)
    [13]
    梁乃生, 脱友才, 邓云等. 基于主成分分析与支持向量机的渠道闸前冰输移与堆积判别模型. 力学学报, 2021, 53(3): 703-713 (Liang Naisheng, Tuo Youcai, Deng Yun, et al. Classification model of ice transport and accumulation in front of channel gates based on PCA-SVM. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(3): 703-713 (in Chinese)

    Liang Naisheng, Tuo Youcai, Deng Yun, et al. Classification model of ice transport and accumulation in front of channel gates based on PCA-SVM. Chinese Journal of Theoretical and Applied Mechanics. 2021, 53(3): 703-713 (in Chinese)
    [14]
    曹蕾蕾, 朱旺, 武建华等. 基于人工神经网络的声子晶体逆向设计. 力学学报, 2021, 53(7): 1992-1998 (Cao Leilei, Zhu Wang, Wu Jianhua, et al. Inverse design of phononic crystals by artificial neural networks. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(7): 1992-1998 (in Chinese)

    Cao Leilei, Zhu Wang, Wu Jianhua, et al. Inverse design of phononic crystals by artificial neural networks. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(7): 1992-1998 (in Chinese)
    [15]
    张珍, 叶舒然, 岳杰顺等. 基于组合神经网络的雷诺平均湍流模型多次修正方法. 力学学报, 2021, 53(6): 1532-1542 (Zhang zhen, Ye Shuran, Yue Jieshun, et al. A combined neural network and multiple modification strategy for Reynolds-averaged Navier-Stokes turbulence modeling. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(6): 1532-1542 (in Chinese)

    Zhang zhen, Ye Shuran, Yue Jieshun, et al. A combined neural network and multiple modification strategy for Reynolds-averaged Navier-Stokes turbulence modeling. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(6): 1532-1542 (in Chinese)
    [16]
    Lei Z, Zhou Y, Sun B, et al. An intrinsic timescale decomposition-based kernel extreme learning machine method to detect tool wear conditions in the milling process. The International Journal of Advanced Manufacturing Technology, 2020, 106: 1-4 doi: 10.1007/s00170-019-04744-5
    [17]
    Nakai ME, Aguiar PR, Guillardi HJ, et al. Evaluation of neural models applied to the estimation of tool wear in the grinding of advanced ceramics. Expert Systems with Applications, 2015, 42(20): 7026-7035 doi: 10.1016/j.eswa.2015.05.008
    [18]
    姜涵文, 高亮, 安博伦等. 基于神经网络的钢轨磨耗与通过总重关联关系的预测方法. 铁道学报, 2021, 43(10): 75-83 (Jiang Hanwen, Gao Liang, An Bolun, et al. A neural network-based prediction approach of relationship between rail wear and gross traffic tonnage. Journal of the China Railway Society, 2021, 43(10): 75-83 (in Chinese)

    Jiang Hanwen, Gao Liang, An Bolun, et al. A neural network-based prediction approach of relationship between rail wear and gross traffic tonnage, Journal of the China Railway Society, 2021, 43(10): 75-83 (in Chinese)
    [19]
    程泽华. 基于人工神经网络的MATLAB接触线磨耗预测模型研究. [硕士论文]. 北京: 中国铁道科学研究院, 2018

    Cheng Zehua. Research on contact wire wear predicting model by using MATLAB based on artificial neural network. [Master Thesis]. Beijing: China Academy of Railway Sciences, 2018 (in Chinese)
    [20]
    Zhang Y, Zhang JW, Luo L, et al. Optimization of LMBP high-speed railway wheel size prediction algorithm based on improved adaptive differential evolution algorithm. International Journal of Distributed Sensor Networks, 2019, 15(10): 1-9
    [21]
    Wang SW, Yan H, Liu CX, et al. Analysis and prediction of high-speed train wheel wear based on SIMPACK and backpropagation neural networks. Expert Systems, 2021, 38(7): e12417
    [22]
    Huang GB, Zhu QY, Siew CK. Extreme learning machine: Theory and applications. Neurocomputing, 2006, 70(1-3): 489-501 doi: 10.1016/j.neucom.2005.12.126
    [23]
    Kapil BS. On extreme learning machine for ε-insensitive regression in the primal by Newton method. Neural Computing and Applications, 2013, 22(3-4): 559-567 doi: 10.1007/s00521-011-0798-9
    [24]
    Gao Z, Hu QG, Xu XY. Condition monitoring and life prediction of the turning tool based on extreme learning machine and transfer learning. Neural Computing and Applications, 2022, 34(5): 3399-3410
    [25]
    丁世飞, 张楠, 史忠植. 拉普拉斯多层极速学习机. 软件学报, 2013, 22(3-4): 559-567 (Ding Shifei, Zhang Nan, Shi Zhongzhi. Laplacian multilayer extreme learning machine. Journal of Software, 2013, 22(3-4): 559-567 (in Chinese)

    Ding Shifei, Zhang Nan, Shi Zhongzhi. Laplacian multilayer extreme learning machine. Journal of Software, 2013, 22(3-4): 559-567 (in Chinese)
    [26]
    Dwivedi V, Srinivasan B. Physics informed extreme learning machine (PIELM)–A rapid method for the numerical solution of partial differential equations. Neurocomputing, 2020, 391: 96-118 doi: 10.1016/j.neucom.2019.12.099
    [27]
    Kasun LLC, Zhou HM, Huang GB, et al. Representational learning with extreme learning machine for big data. IEEE Intelligent System, 2013, 28(6): 31-34
    [28]
    Shebani A, Iwnicki S. Prediction of wheel and rail wear under different contact conditions using artificial neural networks. Wear, 2018, 406-407: 173-184 doi: 10.1016/j.wear.2018.01.007
    [29]
    Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786): 504-507 doi: 10.1126/science.1127647
    [30]
    Jendel T. Prediction of wheel profile wear—Comparisons with field measurements. Wear, 2002, 253(1): 89-99
    [31]
    Piotrowski J, Kik W. A simplified model of wheel/rail contact mechanics for non-Hertzian problems and its application in rail vehicle dynamic simulations. Vehicle System Dynamics, 2008, 46(1-2): 27-48 doi: 10.1080/00423110701586444
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