铁道科学与工程学报2025，Vol.22Issue(3)：1396-1406,11.DOI:10.19713/j.cnki.43-1423/u.T20241918
基于数据特征识别的接触网典型设备状态不良预警
Early warning of typical equipment malfunctions in overhead contact systems based on data feature recognition
摘要
Abstract
With the continuous increase in the operation time of high-speed railways,pantograph-catenary failures caused by abnormal conditions of OCS equipment occur from time to time.To identify potential safety hazards in equipment operation in advance,a typical equipment condition deterioration early warning method based on data feature recognition was proposed.Firstly,a OCS equipment condition deterioration early warning model was proposed.This model was centered around the"data preparation-feature extraction-algorithm construction-experimental verification"axis,providing the main technical points and routes for constructing an early warning method for equipment condition deterioration.Then,using methods such as Kalman filtering,corner feature recognition,and dynamic time warping,algorithms for gross error correction of OCS detection data,positioning point recognition,and mileage correction were presented,providing a solid data foundation for the construction of early warning methods.Finally,based on the actual measurement data from the pantograph-catenary comprehensive detection system,for common geometric conditions,force conditions,and elasticity conditions in OCS operation and maintenance,data features were extracted through various time series data analysis methods.A training sample library was constructed,and three typical equipment condition deterioration early warning methods were obtained through training using outlier diagnosis,random forest,and other algorithms.The obtained typical equipment condition deterioration early warning methods are tested,and various serious equipment condition abnormalities such as mast tilt,cantilever sleeve slippage,positioning component wear,and compensation device jamming are discovered.The overall accuracy rate is 93%,indicating that the proposed method can accurately locate typical equipment condition abnormalities and is feasible for application in OCS operation and maintenance.By analyzing the equipment condition deterioration locations discovered by the algorithm,it is verified that the extracted data features have good separability and can accurately reflect the changes in OCS equipment condition.关键词
OCS/状态预警/特征建模/机器学习/数据预处理Key words
OCS/early warning/feature modeling/machine learning/data preprocessing分类
交通运输引用本文复制引用
王同军,柯在田,王婧..基于数据特征识别的接触网典型设备状态不良预警[J].铁道科学与工程学报,2025,22(3):1396-1406,11.基金项目
中国国家铁路集团有限公司科研项目(N2024G020) （N2024G020）
