Optimized Data Preprocessing and Model Selection for TBM Cutter Wear Prediction

The estimation of disc cutter life is important due to the high equipment replacement cost in tunnel boring machines (TBMs). In this paper, we propose a comprehensive method for predicting disc cutter wear, integrating dimensionality reduction in shield tunneling parameters, data input preprocessing, and optimized model selection. First, we select core TBM operation parameters as model inputs through principal component analysis (PCA) and PCA directly. And, then, based on the characteristics of the operation parameters, we propose a data preprocessing method for time series models. This method uses excavation rings as units, providing a more accurate representation of the actual shield excavation process. Furthermore, by evaluating the prediction accuracy, training efficiency, and inference efficiency of various models, we demonstrate that the combined PCA data + Min Max Scaler + gated recurrent unit (GRU) method achieves the most accurate prediction results while offering real-time prediction capabilities.