Physics-Informed Virtual Sensing for Wind Turbine Blade Degradation Detection Using Flapwise–Edgewise Load Coupling

Early detection of blade structural degradation is critical for wind turbine reliability, as unplanned failures contribute to approximately 25% of operational downtime. Traditional blade condition monitoring methods based on distributed vibration, strain, or acoustic emission sensing often suffer from high hardware complexity and substantial data-processing requirements. Consequently, blade load measurements at the blade root have emerged as a promising and more practical alternative for structural health monitoring. This study introduces a physics-informed virtual sensing framework that combines high-fidelity aeroelastic simulation with machine learning trained exclusively on healthy operational data for blade degradation detection. The proposed method is based on the premise that, under healthy conditions, the relationship between flapwise and edgewise bending moments remains consistent across operating conditions, whereas structural degradation disrupts this coupling and induces measurable deviations from the learned correlation. To implement this concept, OpenFAST simulations of the NREL 5MW reference turbine are conducted under multiple operating conditions with progressively introduced stiffness degradation, forming a comprehensive dataset. A Random Forest model is trained to predict edgewise bending moments from flapwise loads and structural parameters using only healthy-condition data, achieving high predictive accuracy under healthy operating conditions (R2 = 0.98; MAPE = 3.67%). Fault detection is performed by analysing residuals between predicted and simulated edgewise bending moments, which increase systematically when degradation is present. The proposed framework achieved an overall classification accuracy of 88.9%, sensitivity of 88.9%, precision of 96.0%, F1-score of 0.923, and an AUC of 0.951 across all degradation levels and operating conditions, while achieving 100% detection of severe (20%) torsional stiffness degradation cases. Notably, degradation reduces absolute blade loads due to aeroelastic twist-to-feather coupling, yet introduces distinctive load asymmetry patterns that enable effective discrimination. The method demonstrates strong generalisation across the investigated wind speed conditions without retraining. By reconstructing unmeasured edgewise loads from readily available flapwise measurements, the proposed approach enables scalable structural health monitoring of existing wind turbine fleets without hardware retrofitting, offering a practical pathway toward physics-guided digital twin development.