A VMD-EWT-LSTM-based multi-step prediction approach for shield tunneling machine cutterhead torque

Abstract

Cutterhead torque is an important operational parameter that reflects the obstruction degree of geological environment to shield tunneling machine. Accurate multi-step prediction for cutterhead torque is of crucial significance for ensuring efficient and safe propulsion of shield tunneling machine. In this study, a novel hybrid multi-step prediction model combining variational mode decomposition (VMD), empirical wavelet transform (EWT) and long short-term memory (LSTM) network is proposed for shield tunneling machine cutterhead torque. To begin with, the VMD is employed to decompose the original cutterhead torque subsequences into some subsequences and residual sequence, and the residual sequence is further decomposed by the EWT. The combination of VMD and EWT significantly reduces the complexity of the original cutterhead torque sequence. On this basis, the LSTM neural network is employed to predict each subsequence in multiple time steps, and finally add the prediction results of each subsequence to realize the multi-step cutterhead torque prediction. To demonstrate performance of the presented VMD-EWT-LSTM-based approach, comparisons with recent prediction algorithms in six datasets is conducted. The results testify that accuracy of the VMD-EWT-LSTM-based prediction approach is better than other methods. In six datasets, from 1st step prediction to 5th step prediction, the average accuracy of presented prediction approach reaches 97.7%, 97.2%, 96.9%, 96.7% and 96.3%, respectively. Hence, the VMD-EWT-LSTM-based approach can accurately predict cutterhead torque of shield tunneling machine in multiple time steps.