Precise Cutterhead Clogging Detection for Shield Tunneling Machine Based on Deep Residual Networks

During the construction process of tunnels, the cutterhead of shield tunneling machines may get clogged due to clay adhesion, which may seriously affect the efficiency of the project. Therefore, finding an intelligent diagnosis method to detect the clogging status is of great importance. In this study, a deep residual network-based method for diagnosing cutterhead clogging on shield tunneling machines is proposed. First, working state data of the shield tunneling machine is screened out, and parameters reflecting the clogging state are selected for further analysis. After eliminating extreme outliers, an empirical formula is proposed to label the data. At the same time, several time-domain features of the selected excavation parameters within every five minutes are extracted. These features are then fed into the proposed model as the input data to realize clogging detection. Because the original dataset is unbalanced, the combination of f1-score and accuracy is used to evaluate the performance of the proposed model. The results show that the accuracy of the proposed algorithm reaches 95.71%, which is 1.21%, 2.84%, 9.84%, 6.04%, and 0.86% higher than the support vector machine-based, random forest-based, AdaBoost-based, extreme gradient boosting-based and deep neural network-based methods. The f1 score of the proposed model is 0.923, which is also 0.038, 0.042, 0.269, 0.169 and 0.02 higher than those compared methods. Therefore, the proposed deep residual network-based method can accurately detect cutterhead clogging conditions.