A novel LSTM-autoencoder and enhanced transformer-based detection method for shield machine cutterhead clogging

Shield tunneling machines are paramount underground engineering equipment and play a key role in tunnel construction. During the shield construction process, the mud cake formed by the difficult-to-remove clay attached to the cutterhead severely affects the shield construction efficiency and is harmful to the healthy operation of a shield tunneling machine. In this study, we propose an enhanced transformer-based detection model for detecting the cutterhead clogging status of shield tunneling machines. First, the working state data of shield machines are selected from historical excavation data, and a long short-term memory-auto-encoder neural network module is constructed to remove outliers. Next, variational mode decomposition and wavelet transform are employed to denoise the data. After the preprocessing, nonoverlapping rectangular windows are used to intercept the working state data to obtain the time slices used for analysis, and several time-domain features of these periods are extracted. Owing to the data imbalance in the original dataset, the k-means-synthetic minority oversampling technique algorithm is adopted to over-sample the extracted time-domain features of the clogging data in the training set to balance the dataset and improve the model performance. Finally, an enhanced transformer-based neural network is constructed to extract essential implicit features and detect cutterhead clogging status. Data collected from actual tunnel construction projects are used to verify the proposed model. The results show that the proposed model achieves accurate detection of shield machine cutterhead clogging status, with 98.85% accuracy and a 0.9786 F1 score. Moreover, the proposed model significantly outperforms the comparison models.

Automated summary

In this study, an enhanced transformer-based detection model is proposed for detecting the cutterhead clogging status of shield tunneling machines. The model achieves accurate detection with high accuracy and F1 score by using historical excavation data, preprocessing techniques, and an enhanced transformer-based neural network. The proposed model outperforms the comparison models in detecting cutterhead clogging status.