Abstract: The ballastless track systems of high-speed railways directly beared the high-frequency impacts and vibrations of high-speed trains, and the state changes of rail stretch deformation seriously affected trained safety. Traditional periodic maintenances were prone to overhaul or disrepair. A condition monitoring platform for ballastless track systems of high-speed railways was established by using fiber Bragg grating sensing technology, and the monitoring contents and monitoring point layout schemes were proposed. By analyzing the basic characteristics of monitoring data and combining with the track quality index, a back propagation(BP) neural network prediction model for ballastless track states was established， which was used to predict the service stats of rails. Prediction results guide the operation and maintenance departments to carry out timely and reasonable track maintenance and maintenance, so as to achieve preventive maintenance of high-speed rail systems.

Key words: high-speed railway, ballastless track, back propagation(BP) neural network, preventive maintenance, monitor

摘要： 高速铁路无砟轨道系统直接承载高速列车的高频冲击和振动，钢轨伸缩变形等状态变化都会严重影响列车安全，传统周期性检修易出现过修或失修问题，针对此，利用光纤光栅传感技术建立高铁无砟轨道系统状态监测平台，提出监测内容和监测点布置方案。通过对监测数据基本特征的分析，并结合轨道质量指数，建立了无砟轨道状态BP神经网络预测模型，预测了轨道服役状态。预测结果可指导铁路运营维护部门及时、合理地进行轨道养护维修，实现高铁轨道系统的预防性维修。

关键词: 高速铁路, 无砟轨道, BP神经网络, 预防性维修, 监测