## 3 Results and Discussion
### Accelerated Fatigue Tests
Figure 9: Evolution of different acoustic parameters and the cumulative number of events over time during accelerated test. All these events that appeared during the 2200 s of the fatigue test were analyzed following the procedure explained in Section 2.3. A total of 910 events were identified, and machine learning-based classification was performed using the Isolation Forest Algorithm. Results are visualized in pair plots created with the Seaborn library in Python (see Figure 10). In the graph below, outliers are marked in orange while blue marks represent standard events. The features that most distinguish outliers from the rest according to the study are amplitude, rise time, duration, and energy, that is, time domain features. Conversely, no apparent differences between the two classes are observed in relation to the number of counts and peak frequency. Higher frequencies would be expected from cracking events (assuming that outliers are cracking events). For instance, some authors identified peak frequency as the main relevant feature for classifying different cracking mechanisms during corrosion phenomena [29] or compression tests [30]. This finding aligns well with the observations in Figure 9, where regions associated with higher damage level contain the highest peak frequency events. Others have determined that frequencies above 350 kHz are strongly correlated with crack counts in some industrial processes like cladding [31]. When the machine learning-based classification is represented using Principal Component Analysis (PCA), it is observed that the main three components explain 80% of the variability and the two classes (red--outliers and blue--normal or standard signal) are clearly distinguishable (Figure 11). This indicates that both types of signals can be effectively differentiated. As an example, in the following figure (Figure 12), a representative sample of each signal is shown. Outliers (image on the right side) exhibit higher amplitude signals with greater energy, rise time, and duration compared to the other signals (image on the left side). This indicates that the machine learning algorithm is capturing the primary variability which is related to different amplitude signals. However, it is important to note that cracking aspects are better defined by frequency domain features, such as the peak frequency (as observed in Figure 9), rather than amplitude- and energy-related features. Figure 10: Pair plot illustrating features that distinguish outliers from standard signals. In the context of DIC analysis, the graph below (Figure 13) shows the strain evolution of four different points within the sandwich structure. These points correspond to the same position in the longitudinal axis as illustrated in Figure 5. Under normal conditions, without any damage, the four strain values should be at the same level, indicating homogeneous strain distribution. However, in the presence of a damaged fin, a redistribution of the strain would occur, as some parts of the heat exchanger may not be able to transmit the same load. In the graph below, it is observed that from the 750 s onwards (7500 cycles), a redistribution of the strain occurs. Strain values 1 to 3 are higher than 4, located at the right side of the component. This indicates that damage occurred on the left side of the structure where higher deformation levels were achieved. In fact, the structure broke from that exact location. Therefore, the damage was effectively identified by the DIC approach. Figure 11: PCA analysis and the two classes that are distinguished (red points indicate outliers). Figure 12: Comparison between standard or normal signal (**a**) and outlier signal (**b**) showing different amplitude and duration. When comparing this indication with the one provided by the microphone, it can be stated that damage is identified at a later stage with the DIC system.