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.