A novel decomposition and hybrid transfer learning-based method for multi-step cutterhead torque prediction of shield machine

As a core operating parameter of shield machine, the high-precision multi-step prediction of cutterhead torque can effectively help the driver adjust the operating parameters in advance to ensure the safe operation of shield machine. Due to data distribution discrepancy, it is challenging to accurately predict the cutterhead torque under different geological conditions. Currently, the existing methods cannot precisely predict cutterhead torque in cross domain. In order to accurately predict the cutterhead torque under different geological environments, we propose a novel decomposition and hybrid transfer learning-based method for multi-step cutterhead torque transfer prediction. First, original cutterhead torque signal is decomposed into some sub-signals by variational mode decomposition (VMD), which can effectively reduce the complexity. Then hybrid transfer learning-based method (HTLM) is established to predict each sub-signal and add the prediction results to realize multi-step cutterhead torque prediction in cross domain. HTLM has three network branches. The first one is that multi-layer GRU network is used to extract time-varying characteristics of source domain data. The second is that the automatic encoder is used to extract the deep common features between source domain and target domain. The third is the adversarial network and the maximum mean discrepancy (MMD) metric for domain adaptation. Then, the extracted features are integrated to predict the cutterhead torque in cross domain. To verify the proposed VMD-HTLM, several transfer prediction experiments were carried out. From the first step to fifth step transfer prediction, the proposed method achieved higher prediction accuracy than existing methods. Especially in the fifth step, the accuracy of proposed method is on average 7.09 % higher than existing methods, the RMSE and the MAE decreased on average 40.00 % and 39.92 %, respectively. Furthermore, the average accuracy of proposed method is 93.79 %, 93.58 %, 93.55 %, 92.25 % and 91.79 % from first step to fifth step, respectively. Hence, the proposed VMD-HTLM can accurately predict the cutterhead torque under different geological conditions.