轨道交通复合线路轮轨力实时识别网络研究
张泽腾王金海杨建伟姚德臣
Research on Real-time Wheel-rail Force Identification Network of Rail Transit under Compound Line Conditions
ZHANG ZetengWANG JinhaiYANG JianweiYAO Dechen
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作者信息:1.北京建筑大学机电与车辆工程学院，100044，北京
2.城市轨道交通列车服役性能保障北京市重点实验室，100044，北京
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Affiliation:1.School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, 100044, Beijing,China
2.Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles, 100044, Beijing, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2024.08.008
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中图分类号/CLCN:U211.4
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栏目/Col:研究报告
摘要:
［目的］轮轨力对研究轨道交通列车运行状态、车轮不圆及轨道波磨等问题具有重要作用。现有轮轨力识别方法存在数据采集困难、使用成本大，无法识别轨道交通线路条件下的轮轨力等问题，需对轨道交通线路轮轨力实时识别网络进行研究。［方法］基于卷积神经网络构建轨道交通线路轮轨力实时识别网络。根据某型城市轨道交通拖车实测数据，采用SIMPACK软件建立仿真模型。基于仿真数据，研究了轮轨力实时识别网络在不同曲线半径线路和不同列车运行速度等工况下对横向和垂向轮轨力的识别精度与速度。［结果及结论］轮轨力实时识别网络对轮轨垂向力的识别能力优异，其相关系数均能达到0.99，平均绝对误差约为500N且仅为真实值的1%；该网络对轮轨横向力的识别能力随着列车运行速度的提高有所下降，但仍在可接受范围内，其相关系数由0.93降低至0.65，平均绝对误差由1480N提高至3000N且约为真实值的20%。轮轨力实时识别网络具备较快的计算速度，能够满足轮轨力实时识别的需求。
Abstracts:
［Objective］ Wheel-rail force plays an important role in the study of train running state, wheel off roundness and rail corrugation in rail transit. Current wheel-rail force identification methods have some problems, such as difficulty in data collection, high cost and inability to identify wheel-rail force under rail transit line conditions, so it is necessary to study the real-time wheel-rail force identification network of rail transit lines. ［Method］ A real-time wheel-rail force identification network of rail transit lines is constructed based on convolutional neural network. According to the measured data of a type of urban rail transit trailing car, the simulation model is established using SIMPACK software. Based on the simulation data, the accuracy and speed of the real-time wheel-rail force identification network for the lateral and vertical wheel-rail forces under working conditions, such as different curve radii lines and different train running speeds are studied. ［Result & Conclusion］ The real-time wheel-rail force identification network has an excellent ability to identify the wheel-rail vertical force, with the correlation coefficients all up to 0.99, and the average absolute error about 500 N and only 1% of the true value. The identification ability of the network for wheel-rail lateral force decreases with the increase of train operating speed, but it is still within the acceptable range. The correlation coefficient decreases from 0.93 to 0.65, and the average absolute error increases from 1 480 N to 3 000 N, which is approximately 20% of the actual value. The proposed network has a fast calculation speed and can meet the needs of wheel-rail force real-time identification.
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