地铁轨道不平顺状态的监测与预警

Monitoring and Early Warning of the Irregularity of Subway Track

在客流量和客运量高速化的背景下,轨道线路作为地铁运营的基本设施,其不平顺状态直接影响到行车的安全性和舒适性。以北京地铁9号线为例,采用光纤光栅传感器对典型区段的轨道不平顺状态进行监测,运用数理统计的方法对监测数据进行权重统计和超限分析,结合数据特征提出一种将灰色模型和粒子群算法优化Elman神经网络的组合预测方法,并利用监测的轨道不平顺七项参数对模型的效果进行验证。结果表明该模型的预测效果均优于单一灰色模型,在平均相对误差、均方根误差、决定系数和相关系数上具有较高的预测精度,可以实现对各单项指标的超限预警,更好地监控轨道质量的发展状况。

Under the background of high traffic volume and passenger traffic,the track line is the basic facility for subway operation,and its irregularity directly affects the safety and comfort of driving.Take Beijing Subway Line nine as an example,the fiber grating sensor was used to monitor the track irregularity of a typical section.The statistics and the over-limit analysis of the monitoring data were performed by mathematical statistics.A combined prediction method combining gray model and particle swarm optimization for Elman neural network was proposed by combining with the data characteristics.The effects of the model were verified by the seven parameters of track irregularity monitored.The results show that the prediction effect of the model is better than that of the single gray model.It has high prediction accuracy in terms of average relative error,root mean square error,determination coefficient and correlation coefficient.It can achieve over-limit warning for each individual indicator,which can better monitor the development of track quality.