Turnout fault prediction method based on gated recurrent units model  

基于门控循环单元模型的道岔故障预测方法

作　　者：ZHANG Guorui SI Yongbo CHEN Guangwu WEI Zongshou 张国瑞;司涌波;陈光武;魏宗寿(兰州交通大学自动控制研究所,甘肃兰州730070;甘肃省高原交通信息及控制重点实验室,甘肃兰州730070)

机构地区：[1]Automatic Control Research Institute, Lanzhou Jiaotong University, Lanzhou 730070, China [2]Gansu Provincial Key Laboratory of Traffic Information Engineering and Control, Lanzhou 730070, China

出　　处：《Journal of Measurement Science and Instrumentation》2021年第3期304-313,共10页测试科学与仪器（英文版）

基　　金：National Natural Science Foundation of China(Nos.61863024,71761023);Funding for Scientific Research Projects of Colleges and Universities in Gansu Province(Nos.2018C-11,2018A-22);Natural Science Fund of Gansu Province(No.18JR3RA130)。

摘　　要：Turnout is one of the important signal infrastructure equipment,which will directly affect the safety and efficiency of driving.Base on analysis of the power curve of the turnout,we extract and select the time domain and Haar wavelet transform characteristics of the curve firstly.Then the correlation between the degradation state and the fault state is established by using the clustering algorithm and the Pearson correlation coefficient.Finally,the convolutional neural network(CNN)and the gated recurrent unit(GRU)are used to establish the state prediction model of the turnout to realize the failure prediction.The CNN can directly extract features from the original data of the turnout and reduce the dimension,which simplifies the prediction process.Due to its unique gate structure and time series processing features,GRU has certain advantages over the traditional forecasting methods in terms of prediction accuracy and time.The experimental results show that the accuracy of prediction can reach 94.2%when the feature matrix adopts 40-dimensional input and iterates 50 times.

关 键 词：TURNOUT CLUSTERING convolutinal neural network(CNN) gated recurrent unit(GRU) fault prediction 

分 类 号：U21[交通运输工程—道路与铁道工程]