Exploratory data analysis and interpretable machine learning for anomaly detection in Epb tunnel boring machine operations

Introduction Earth-Pressure-Balance Tunnel Boring Machines (EPB TBMs) generate high-dimensional sensor streams during excavation, yet operational anomalies are difficult to identify early because the signals are noisy, coupled, and change with geology and operating mode. Methods This study develops an exploratory data analysis framework for EPB TBM operations using ring-based data from a 7 km tunneling project comprising more than 4,400 rings. After preprocessing and z-score normalization, Pearson and Spearman correlation analyses confirmed strong torque–thrust coupling (r ≈ 0.85). Time-series visualization with z-score outlier screening identified rings showing high thrust but low penetration, indicating potential tool inefficiency, wear, or challenging ground conditions. An XGBoost regression model was used to predict thrust force, and SHAP analysis quantified feature contributions after excluding thrust-derived and internally redundant indices, identifying cutterhead torque, alignment position, and penetration per revolution as the principal independent predictors. Results The model achieved R 2 = 0.872, RMSE = 381.8 kN, and an a20-index of 0.993 on the held-out test set. Discussion The framework captures both expected physical couplings and orthogonal behaviors, enabling interpretable anomaly identification and providing actionable insights for monitoring and decision support in EPB TBM operations.