摘要
钢轨侧磨是决定重载铁路小半径曲线钢轨服役寿命的关键因素。为研究朔黄重载铁路曲线钢轨侧磨发展规律,基于钢轨磨耗和线路设备、运营、修理等数据,分析不同半径曲线的钢轨侧磨发展规律特征,建立基于非线性自回归神经网络的曲线钢轨侧磨发展预测模型,并提出适用于朔黄重载铁路的曲线钢轨换轨策略。研究结果表明,朔黄重载铁路曲线钢轨侧磨满足先慢后快的分段发展规律;钢轨侧磨发展受多种因素影响,在相同曲线半径、相同钢轨材质下钢轨侧磨也可能具有不同的发展规律;对于半径大于800 m的曲线钢轨,侧磨不是换轨大修的决定因素;模型预测的均方根误差和相关系数分别为0.74 mm、0.84,预测值与实际值具有强相关性;提出状态修与周期修相结合的朔黄重载铁路曲线钢轨换轨策略,为提前制定换轨计划、保障钢轨合理使用提供帮助。
Rail side wear is the key factor influencing the service life of sharp curve rail in heavy haul railway. In order to study the development law of curve rail side wear in Shuohuang heavy haul railway, the development features of rail side wear under various curve radiuses are analyzed, a prediction model of curve rail side wear based on nonlinear autoregressive neural network(NARX neural network) is established, and the rail replacement strategy for Shuohuang heavy haul railway is proposed, based on the long-term data of rail side wear, line equipment, operation and repair. The results show that the curve rail side wear in Shuohuang heavy haul railway grows from slow to fast, following a two-phase development law. Rail side wear is affected by many factors, and the development law of rail side wear may vary even under the same curve radius and the same rail material. As for the curves with radius greater than 800 m, side wear is not the dominant factor of rail replacement. The root mean square error and the correlation coefficient of the prediction model are 0.74 mm and 0.84 respectively, showing a strong correlation between the predicted value and the actual value. Finally, a rail replacement strategy based on the combination of state repair and periodic repair for Shuohuang heavy haul railway curve rail is proposed, so as to provide help for arranging rail replacement plans and ensuring proper use of the rail.
作者
马帅
刘秀波
任松斌
陈茁
刘玉涛
MA Shuai;LIU Xiubo;REN Songbin;CHEN Zhuo;LIU Yutao(Infrastructure Inspection Research Institute,China Academy of Railway Sciences Co.,Ltd.,Beijing 100081;Line Inspection and Rescue Center,China Energy Shuohuang Railway Development Co.,Ltd.,Suning 062350;Shenzhen Research and Design Institute of CARS,Shenzhen 518034;Shenzhen Engineering Laboratory for Vibration and Noise Reduction of Urban Rail Transit,Shenzhen 518034)
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2021年第18期118-125,共8页
Journal of Mechanical Engineering
基金
国家能源投资集团有限责任公司科技创新资助项目。
关键词
重载铁路
小半径曲线
钢轨侧磨
预测模型
换轨
heavy haul railway
sharp curve
rail side wear
prediction model
rail replacement
