Physics‐Informed Neural Networks for Fast Identification of Internal Defect Depths and Thicknesses via Thermographic Inspection

This article deals with the use of unsteady thermographic inspection as a structural health monitoring method for fast and accurate detection of internal defects in wind turbine blades. Temperature transients allow estimation of both the thickness of internal anomalies and the depth at which they are embedded. On the other hand, the use of physics‐informed neural networks to predict heat transients in the framework of the solution to the inverse problem in the proposed methodology results in a very low computational cost of the identification algorithm. As a result, the proposed technique can be easily implemented in an instrumented unmanned aerial vehicle (UAV), where onboard processing resources could be rather limited. The developed methodology is applied to two defect identification scenarios. In the first one, active heating (provided by four 250 W lamps) is used to induce thermal transients on the blade surface. In this case, even deep defects can be very accurately identified by the proposed technique. A second identification scenario considers passive heating (through solar radiation) to save battery consumption in order to extend the UAV flight time. In this case, identification accuracy depends on the rate of change in solar radiation. Although the identification errors are larger than in the previous case (with active heating), both depth and thickness can be identified with rather small errors in the considered examples.