The shield cutterhead torque reflects the mechanical interaction characteristics between the cutterhead and the stratum.Accurately predicting torque changes in real-time can help adjust tunnelling parameters in ad-vance,ensure smooth machine operation,and reduce cutting tool wear.Therefore,this paper proposes a deep learning model based on Temporal Convolutional Network(TCN)and Long Short-Term Memory(LSTM)for real-time predic-tion of cutterhead torque.The results indicate that the TCN-LSTM model can capture the local features of the input parameters and establish long-term dependencies,achieving the highest prediction accuracy compared to other models.The model performs stably in multi-step predictions,enabling longer lead-time predictions of cutterhead torque.A 4∶1∶1 data set split ratio yields the optimal performance for the prediction model.
关键词
刀盘扭矩/时间卷积网络/长短时记忆网络/多步预测/划分比例
Key words
Cutterhead torque/Temporal Convolutional Network/Long Short-Term Memory Network/Multi-step prediction/Split ratio
维普
万方数据