## 3 Results and Discussion
This discrepancy can be reduced by an even better matching of a physical and numerical model or by the integration of specific system data before the classification stage. The latter strategy was chosen here in the form of including an expert sample in the decision threshold calculation. In the actual classification task, the transformed data is further utilized to calculate the corresponding damage index of each unseen sample as outlined in Section 2.4. When plotting the damage indices of all evaluated samples, depicted as colored points in Figure 11, there is again a clear distinction between the different structural states. The lowest bound of the benchmark set \(\widetilde{\mu}^{R}\) and the threshold \(t\) are additionally represented by horizontal lines. For the tolerance parameter, a value of \(\gamma=0.3\) was used. The model classifies every  
Figure 10: Two-dimensional MDS embedding of the transformed experimental data and 40 randomly picked benchmark residuals. The benchmark residuals are marked by crosses. Figure 9: Visualization of the residuals \(\delta_{j}\) of each feature. All transformed experimental samples are shown. The corresponding data points are marked by dots. Additionally, 5 randomly picked samples from the benchmark set are visualized, marked with a cross. The distance between residuals is largest at sensor 8, clearly separating pristine from damaged samples. sample with a damage index above the threshold as damaged and below it as pristine. This is consistent with the labeling of the data points; the model has a classification accuracy of 100 %. It should be noted that the computation of the threshold \(t\) is partly based on the empirical parameter \(\gamma\), which controls the sensitivity of the damage detection. While the approach is intuitive, a more rigorous formulation remains a subject for future research. One possible direction could involve linking \(\gamma\) to a statistical confidence level or to a similarity metric that quantifies how far the expert sample lies from the distribution of synthetic pristine data. A further examination of the transformed data depicted in Figure 9 has shown that the model also allows specific conclusions to be drawn about the discrepancies between numerical and experimental model. This can be observed in the high residual values for all structural states at sensor 4, which suggest a systematic error. A closer inspection of the sensor grid on the spoiler demonstrator revealed that most sensors do not align perfectly with the positions used to extract strain data from the numerical model. Particularly, sensor 4 is slightly rotated counter-clockwise while all others are rotated clockwise. It can be shown that a rotational transformation of the measured strain data, which is easily achieved in a post-processing step, reduces the residual values at sensor 4. Despite the misalignment between the numerical and experimental sensor grid and the almost unavoidable differences in the strain state due to modeling abstractions, the method is able to correctly classify damages. This confirms the method's practical relevance and highlights its potential to detect real damage based solely on numerically generated pristine data. In principle, the proposed SPU architecture is applicable to arbitrary structural shapes and sensor grid layouts, provided the sensor density suffices to measure effects of relevant discontinuities, and a pristine training set representing diverse loading scenarios is available. The strain field \"fingerprint\" of the structure is captured during training, while deviations at individual strain sensing points, which are not supported by the predictions of the ensemble of the remaining SPUs, indicate anomalies. This architecture is independent of the structure's geometry, as well as the spatial layout of the sensor grid, though full validation remains for future work. Training with synthetic data additionally requires accurate knowledge and pairing of the sensor positions, as shown previously.