铁道科学与工程学报2025，Vol.22Issue(5)：2367-2379,13.DOI:10.19713/j.cnki.43-1423/u.T20241104
基于MultiCNN-GRU-ITA的动车组牵引电机温度预测模型
Temperature prediction model of traction motors in EMUs based on MultiCNN-GRU-ITA
摘要
Abstract
The temperature prediction is crucial for the condition assessment and daily maintenance of traction motors in high-speed trains.Aiming at the problem of insufficient feature extraction of traction motor timing data in existing timing prediction models,resulting in low prediction accuracy,a MultiCNN-GRU-ITA based temperature prediction model for high-speed train traction motors was proposed.The model could predict the temperature by deeply extracting spatiotemporal features of the extra data.This model proposed a multi-channel convolutional neural networks(MultiCNN)module,which obtained the spatial features of traction motor data at multiple scales and enhances the representation ability of features.It designed a GRU stack module,which uses Gated Recurrent Unit(GRU)to capture long-term dependencies,extracted the temporal features from the traction motor data,and more accurately predicted dynamic temperature changes.The Improved Attention Mechanism(ITA)module was introduced to focus on key information in spatiotemporal features,further enhancing the model's ability to recognize important features.The dataset used in this study was created using actual operational data from high-speed trains,and experiments were conducted in various prediction scenarios.The experimental results show that the MultiCNN-GRU-ITA model exhibits significant advantages in MAE and MSE in four scenarios with predicted output step sizes of 5 minutes,10 minutes,15 minutes,and 20 minutes.Compared with LSTM,GRU,SVR,and ARIMA models,the MAE and MSE indicators are reduced by more than 41.03%and 65.32%,respectively.Under different prediction intervals,the temperature prediction curves of the MultiCNN-GRU-ITA model exhibit a high degree of fit with the actual values.This model can effectively capture the temperature change trend of the traction motor and provide model support for constructing a high-precision traction motor fault prediction and health assessment system.关键词
牵引电机/温度预测/多通道卷积神经网络/门控循环单元/注意力机制Key words
traction motor/temperature prediction/multi-channel convolutional neural network/gated recurrent unit/attention mechanism分类
交通工程引用本文复制引用
王运明,李明阳,陈梦华,常振臣..基于MultiCNN-GRU-ITA的动车组牵引电机温度预测模型[J].铁道科学与工程学报,2025,22(5):2367-2379,13.基金项目
国家自然科学基金资助项目(62103074) （62103074）
辽宁省教育厅基本科研项目(LJKMZ20220857) （LJKMZ20220857）
辽宁省交通科技项目(202345) （202345）
