Transfer component analysis-driven domain adaptation approach for estimating the life of tunnel boring machine disc cutters

Abstract

Estimating the life of disc cutters in tunnel boring machines is crucial for tunnel excavation efficiency. Accordingly, generating accurate predictions of the state of disc cutters can prevent unexpected downtimes while optimizing cutting performance. The literature presents investigations of the application of machine learning (ML) for estimating the disc cutter life. Nevertheless, diverse excavation conditions and data scarcity have stimulated research on transfer learning (TL) frameworks that tailor models for different domains, that is, domain adaptation (DA), by generating a common subspace in which the discrepancies among them are minimized. This study presents an end-to-end framework to estimate the disc cutter life through transfer component analysis, a DA-based method for projecting different domains by considering excavation project datasets in a common space; then, different ML models are trained, including linear, probabilistic, and neural methods. The results revealed that the Gaussian process regression model was superior to other methods, with R2 = 0.99. In conclusion, TL is a valuable tool for estimating disc cutter life across different domains, enabling accurate predictions and leveraging knowledge to mitigate data scarcity challenges. In addition, an explainable AI analysis is conducted to quantify and identify relevant features impacting negatively on the disc cutter life, evidencing great contribution from the cutter rotation speed, quartz content, screw rate and specific energy.