Efficient Torque Prediction for Digital Twins in Quarry Operations: A Data-Driven and Expert-Guided Approach

Quarry sites present unique operational challenges where the performance of heavy machinery is critical for maintaining efficiency and safety. In such environments, accurate torque prediction is essential for effective engine management and optimal task execution. This study addresses the torque prediction challenge for a wheel loader operating in quarry conditions by proposing a structured three-phase approach to feature selection that reduces model complexity while preserving predictive accuracy. In the first phase, features are selected based on domain expertise to capture the physical and operational realities of quarry machinery. A comprehensive set of features is then employed to establish a robust performance baseline. In the final phase, a data-driven analysis using SHapley Additive Explanations (SHAP) identifies the top five features that most significantly impact torque prediction. Model efficacy was validated via cross-validation, with R-squared and mean-squared error serving as the key performance indicators. Comparative analysis reveals that while SHAP-ranked features yield statistically optimal results, the expert-selected features are more aligned with the practical requirements of quarry operations. These findings support the design of efficient, interpretable digital twins for real-time decisions in challenging environments.