UAV-Mounted infrared thermography–driven defect detection framework for wind turbine blades

Accurate online detection and quantitative evaluation of subsurface defects are essential for improving the reliability of wind energy systems. To overcome the limitations of conventional non-destructive testing techniques, which are typically confined to laboratory or manufacturing settings, this study proposes an inverse problem-solving framework that integrates drone-mounted active infrared thermography with Physics-Informed Neural Networks (PINNs). Transient surface temperature data of defect-containing glass fiber reinforced polymer (GFRP) specimens are obtained via an infrared imaging system deployed on an Unmanned Aerial Vehicle (UAV) platform. A PINN-based forward model is established to simulate heat conduction within the material, and Bayesian Optimization (BO) is integrated into the PINN framework (BO-PINN) to construct an inverse solver that simultaneously estimates defect depth and thickness from surface temperature data. Experimental results demonstrate that the proposed model achieves a depth estimation error below 2% and a relative thickness error below 10%, significantly outperforming conventional methods. This work provides a novel predictive framework and technical route for the non-destructive evaluation and service life prediction of GFRP composite structures.