Figure 8. (a) The segmented paint damage area mapped to the TP. (b) The paint damage area in the original image. The paint damage is shown both in (a) and (b) with a collection of blue points.
The corresponding paint damage in the original drone image file is shown as a reference in Figure 8 (b). The paint damage is represented by the blue points in both of these figures. The surface area of the mapped paint damage is approximately 3.1 cm2. This particular paint damage is only a small part of the damages that make up cluster 5 on the TP and the same damage shape can be found in different places scattered around the upper parts of the TP, see Figure 6 (b). It is seen from the figures that the position, shape, and size, relative to the TP and the surroundings, is the same for the paint damage shown in Figures 8 (a) and (b), this must mean that the algorithms used for finding and mapping the paint damages presented here are accurate. The accuracy depends on the geometry of the large-scale structure and the mapping algorithm as discussed in section 3. The segmentation algorithm and its settings are also important for the model’s accuracy. The specific settings for the color threshold segmentation algorithm determine the shape of the paint damage that is mapped to the tower. The shapes and sizes of the paint damages were estimated from a large number of images of the transition piece. This is a technique that is currently in use by many manufacturers. Great care was taken in the selection of the color threshold settings to ensure that the paint damages shapes and sizes were very close to the values found during manual image inspection.
Demonstration of mapping delamination damage on rotor blade
In this demonstration, a composite rotor blade structurally tested in a previous study [25] is used to map delamination damage in a 3D geometry model generated for finite element simulation. The blade was subject to cyclic loading in a full-scale structural testing laboratory, see Figure 9. Delamination damage occurred inside the loading carrying spar cap laminates and can be visually inspected. Detailed information on the experiment and damage detection is not presented here but can be found in [25]. The current study only uses the damage inspection images and maps them to a numerical model and creates a visual digital twin.