Research on vibration feature-driven prediction method of TBM cutter speed

Accurate prediction of the rotational speed of the cutter disc of the tunnel boring machine (TBM) is crucial for improving construction efficiency and reducing equipment loss.This study proposes a vibration feature-driven method for predicting TBM cutter speed based on a plateau railway.Through time alignment, data processing, and feature construction, 15 key features are selected as input parameters of the model through correlation analysis.The cutter speed prediction is carried out using the Random Forest and XGBoost models, and the effect of these models is finally evaluated and compared using data from the test set.The results show that the Random Forest model is more effective in predicting the rotational speed of the cutter disc of TBM, and the R 2 is 0.824.The method proposed in this study achieves the prediction of cutter disc rotational speed based on vibration characteristics, which provides a reference for the construction of TBM.