北京交通大学学报2025，Vol.49Issue(3)：33-43,11.DOI:10.11860/j.issn.1673-0291.20240052
基于1D-CNN和Transformer模型的道岔健康状态预测
Prediction of turnout health status based on 1D-CNN and Transformer models
摘要
Abstract
To address the high failure rate,low maintenance efficiency,and challenges in predicting the health status of railway turnouts,this study proposes a predictive method based on the integration of a One-dimensional Convolutional Neural Network(1D-CNN)and a Transformer model,using the S700K turnout machine as the research object.First,1D-CNN is employed to extract features from the raw data,generating 10 feature sets after training.Then,through feature evaluation,the five most representative feature sets for assessing turnout health are selected.These features,along with the health label values derived from the turnout power curve,are used to train the Transformer model,yielding the predicted health index.Finally,to evaluate the health status of the turnout system,Fisher's optimal segmentation algorithm is used to classify health stages,determining the optimal num-ber of health levels as three.Guidance is provided for maintenance work at different health stages.The research results indicate that the combined 1D-CNN and Transformer model exhibits superior predic-tive performance and generalization ability.Compared to commonly used models such as Gated Recur-rent Unit(GRU)and Long Short-Term Memory(LSTM),the Transformer model achieves better performance in processing long time-series data.The proposed hybrid model significantly improves the accuracy of turnout health status prediction,reducing Mean Absolute Error(MAE)and Root Mean Square Error(RMSE)by 31.2％and 30.5％,respectively,compared to the 1D-CNN and LSTM model combination.关键词
铁路道岔/一维卷积神经网络/Transformer模型/健康状态预测/Fisher最优分割Key words
railway turnouts/one-dimensional convolutional neural network(1D-CNN)/Trans-former model/health status prediction/Fisher's optimal segmentation分类
交通工程引用本文复制引用
陈俊竹,陈光武,石建强,邢东峰..基于1D-CNN和Transformer模型的道岔健康状态预测[J].北京交通大学学报,2025,49(3):33-43,11.基金项目
甘肃省科技重大专项(21ZD4WA018) （21ZD4WA018）
甘肃省高校产业支撑计划项目(2023CYZC-32) （2023CYZC-32）
高校科研创新平台重大培育项目(2024CXPT-17) （2024CXPT-17）
国铁集团科技计划项目(N2022G010) （N2022G010）
兰州市人才项目(2023-QN-118)Gansu Province Major Science and Technology Special Project(21ZD4WA018) （2023-QN-118）
Gansu Province University Industry Sup-port Plan Project(2023CYZC-32) （2023CYZC-32）
Major Cultivation Project of University Research and Innovation Platform(2024CXPT-17) （2024CXPT-17）
China Railway Group Technology Plan Project(N2022G010) （N2022G010）
Lanzhou Talent Project(2023-QN-118) （2023-QN-118）
