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-MLELM),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.
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.
作者
王美琪
王艺
陈恩利
刘永强
刘鹏飞
Wang Meiqi;Wang Yi;Chen Enli;Liu Yongqiang;Liu Pengfei(State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures,Shijiazhuang Tiedao University,Shijiazhuang 050043,China;School of Mechanical Engineering,Shijiazhuang Tiedao University,Shijiazhuang 050043,China)
出处
《力学学报》
EI
CAS
CSCD
北大核心
2022年第6期1720-1731,I0005,共13页
Chinese Journal of Theoretical and Applied Mechanics
基金
国家自然科学基金(12102273,12072205,52072249)
河北省科技计划(20310803 D)资助项目.
关键词
车轮踏面磨耗
极限学习机
磨耗预测
模型辨识
高速列车
wheel tread wear
extreme learning machine
wear prediction
model identification
high-speed train
