# Active Thermography for the Detection of Sub-Surface Defects on a Curved and Coated GFRP-Structure
## 4 Results and Discussion
### Influence of the Angular Perspective on Sub-Surface Defect Detectability  
An essential characteristic of a rotor blade leading edge is its strong curvature. In reality, defects such as air inclusions do not always occur directly at the front of the leading edge, but also to the left or right of it. In addition, while inspecting a rotor blade leading edge, the interlocked thermographic system consisting of the infrared camera and the excitation unit may not always be aligned perpendicular to the surface. To investigate this case, the test specimen in test series 3 was rotated by the angle \(\alpha=25^{\circ}\) as well as by the angle \(\beta=45^{\circ}\) to the optical axis of the thermographic system at an excitation time of \(t_{\text{exc}}=25\) s; see Figure 4. The results of this investigation are shown in Figures 9 and 10.  
Figure 9 shows the examination of the test specimen at an angle of \(\alpha=25^{\circ}\). As in the test series before, the defects are already visible after the pre-processing (Figure 9b,d). The filtering increases the contrast, and all three defects are successfully detected (Figure 9c,e). However, here, the CNR is smaller compared to the images where the camera is oriented head-on to the defect area. Furthermore, since the excitation is rotated \(25^{\circ}\) to the defect area, it is heated less and the CNR decreases. Despite the lower CNR and the high depth-to-diameter ratio of 0.75 of defect 3, it is still successfully detected. The same trend continues in the images taken at an angle of \(\beta=45^{\circ}\); see Figure 10. Again, the CNR decreases because of the increased angle between the defects, the excitation and the camera. Even in this test scenario, defect 3 is still successfully detected.  
To conclude, defects that do not lie in the optical axis of the camera due to the curvature of a target can still be detected, but they stand out from their surroundings with a lower intensity than the defects that have a smaller or no angular deviation from the optical axis  
Figure 8: Mean CNR averaged over distance \(k\) at different \(t_{\text{exc}}\) of the defects 1 to 6 with error bars representing the standard deviation of the averaged CNR value, (**a**) reference data with filtering (ref)—note: defect 5 and 6 could not be detected, (**b**) PCA-based data with filtering (PCA), (**c**) legend.  
of the camera. For the application at the leading edge of the rotor blade, this means that the measuring system does not have to be aligned head-on to the leading edge if this is not possible. Defects located slightly off-center of the image area at a curve can still be detected as long as the area is heated adequately and the depth of field of the camera is sufficient.  
Figure 9: Results of test series 3 (\(\alpha=25^{\circ}\), ROI A, \(t_{\text{exc}}=25\,\text{s}\), \(t_{\text{rec}}=15\,\text{s}\), \(k=89\,\text{mm}\), (**a**) surface temperature of the whole camera FOV, (**b**) result for the reference method without filtering at \(t_{\text{rec}}=0\,\text{s}\), (**c**) reference image with filtering, automatic defect detection and calculation of the CNR, (**d**) result for the PCA-based method without filtering, (**e**) PCA-based image with filtering, automatic defect detection and calculation of the CNR.