Research on Real-time Prediction of Shield Cutterhead Torque Based on TCN-LSTM
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.
Cutterhead torqueTemporal Convolutional NetworkLong Short-Term Memory NetworkMulti-step predictionSplit ratio
万方数据
维普
