A Novel Method for Aging Prediction of Railway Catenary Based on Improved Kalman Filter

作     者：Jie Li Rongwen Wang Yongtao Hu Jinjun Li 

作者机构：School of Electrical Engineering and AutomationHenan Institute of TechnologyXinxiang453003China Technology CenterSichuan Injet Electric Co.Ltd.Deyang618000China Embedded System Research InstituteXinxiang Engineering Research Center for Intelligent Condition Monitoring of MachineryXinxiang453003China 

出 版 物：《Structural Durability & Health Monitoring》 (结构耐久性与健康监测（英文）)

年 卷 期：2024年第18卷第1期

页      面：73-90页

核心收录：

学科分类：08[工学] 0814[工学-土木工程] 082301[工学-道路与铁道工程] 0823[工学-交通运输工程] 

基　　金：supported by the Science and Technology Research Project of Henan Province (No.222102210087) the Science and Technology Research Project of Henan Province (No.222102220102) 

主　　题：Railway catenary Takagi-Sugeno fuzzy neural network Kalman filter aging prediction 

摘      要：The aging prediction of railway catenary is of profound significance for ensuring the regular operation of electrified ***,in real-world scenarios,accurate predictions are challenging due to various *** paper addresses this challenge by proposing a novel method for predicting the aging of railway catenary based on an improved Kalman filter(KF).The proposed method focuses on modifying the priori state estimate covariance and measurement error covariance of the KF to enhance accuracy in complex *** comparing the optimal displacement value with the theoretically calculated value based on the thermal expansion effect of metals,it becomes possible to ascertain the aging status of the *** improve prediction accuracy,a railway catenary aging prediction model is constructed by integrating the Takagi-Sugeno(T-S)fuzzy neural network(FNN)and *** this model,an adaptive training method is introduced,allowing the FNN to use fewer fuzzy *** inputs of the model include time,temperature,and historical displacement,while the output is the predicted ***,the KF is enhanced by modifying its prior state estimate covariance and measurement error *** modifications contribute to more accurate ***,a low-power experimental platform based on FPGA is implemented to verify the effectiveness of the proposed *** test results demonstrate that the proposed method outperforms the compared method,showcasing its superior performance.