数字孪生在列车曲线通过性能预测中的应用研究  

Application of Digital Twin to Curve Negotiation Performance Prediction of Train

作　　者：董少迪 唐兆[1] 王开云[1] 王建斌[1] 黎荣[2] 张建军 DONG Shaodi;TANG Zhao;WANG Kaiyun;WANG Jianbin;LI Rong;ZHANG Jianjun(State Key Laboratory of Traction Power,Southwest Jiaotong University,Chengdu 610031;School of Mechanical Engineering,Southwest Jiaotong University,Chengdu 610031;National Centre for Computer Animation,Bournemouth University,Bournemouth BH125BB,UK)

机构地区：[1]西南交通大学牵引动力国家重点实验室,成都610031 [2]西南交通大学机械工程学院,成都610031 [3]伯恩茅斯大学英国国家计算机动画中心,伯恩茅斯BH125BB英国

出　　处：《机械工程学报》2022年第18期240-250,共11页Journal of Mechanical Engineering

基　　金：国家重点研发计划(2020YFB1711402);四川省自然科学基金(2022NSFSC0415)资助项目。

摘　　要：提出一种基于数字孪生技术的列车曲线通过性实时预测方法,解决传统动力学仿真方法在列车曲线通过性能分析时,面临的多自由度耦合模型构建复杂、不确定性因素分析困难等问题,提高仿真结果的实时性与精确度。构建面向列车曲线通过安全性的数字孪生体,可视化呈现列车曲线通过时安全性指标的动态变化过程。利用MQRNN深度学习算法稳健高效的特点,对列车曲线通过时的构架横向加速度、轮轴横向力、轮轨垂向力、脱轨系数等安全性指标进行特征提取,动态仿真以及实时预测,并将结果与LSTM计算结果进行比较。结果表明,相对LSTM方法,提出的MQRNN方法将最大误差,最大绝对误差分别降低至0.017, 0.09,同时具有更好的抗干扰能力,可以给出置信区间为90%的预测结果。所研究为列车曲线通过数字孪生体的构建及安全性预警奠定了基础。A digital twin method for predicting the safety performance of train curve negotiation is proposed to overcome these challenges posed by multiple-degree-of-freedom coupling modelling and the uncertainty factors analysis in traditional dynamics simulations, and to become more accurate and real-time. A digital twin for the safety prediction of train curve negotiation is built, and the dynamic safety indicators are visualized when a train passes a curved rail. The robustness and efficiency of the deep learning algorithm of MQRNN are helpful to extract features, simulate and predict the safety indicators of lateral acceleration of the frame,lateral force of the wheel shaft, the vertical force of the wheel and rail, as well as derailment coefficient in real-time. The results show that compared with the LSTM method, the proposed MQRNN method reduces the maximum error to 0.017 and 0.09, respectively,and gives prediction results with a 90% confidence interval, demonstrating its superior anti-interference ability. The proposed method can serve as a foundation for further digital twin-based decision-making of the train curve negotiation.

关 键 词：列车曲线通过 数字孪生体 深度学习 实时预测 

分 类 号：U270[机械工程—车辆工程]