铁路基础设施位移数据预测模型研究

Prediction model for displacement data of railway infrastructure

线路安全是铁路运营的重要前提,我国铁路跨度广、行车环境复杂,当铁路基础设施稳定性产生改变时往往会严重影响行车安全。文章采用长短期记忆(LSTM,Long Short-Term Memory)模型对基于全球导航卫星系统(GNSS,Global Navigation Satellite System)的铁路基础设施监测系统的形变监测数据进行建模预测,实现对铁路基础设施灾害的早期预警,并与多种传统时间序列预测模型进行对比,结果表明,LSTM模型具有更好的性能。

Railway safety is an important prerequisite for operation.Due to the wide span and complex driving environment of China's railway,when the stability of railway infrastructure changes,it will seriously affect the driving safety.This paper used Long Short-Term Memory(LSTM)model to model and predict the deformation monitoring data of railway infrastructure monitoring system based on Global Navigation Satellite System(GNSS),Achieved early warning of railway infrastructure disasters,and compared it with various traditional time series prediction models.The experimental results show that the LSTM model has better performance.