## 5 Results  
The validation of the framework relies on analyzing a single current signal of phase A. The simulation time to produce this signal is 5 s and the sampling frequency is 20 kHz, resulting in a signal length of 100,000 samples. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Dimensionality** & **Activation Function** \\ \hline Input & 2000 & - \\ \hline Encoder 1 & 1800 & ReLU \\ \hline Encoder 2 & 1400 & ReLU \\ \hline Bottleneck & 600 & Tanh \\ \hline Decoder 1 & 1400 & ReLU \\ \hline Decoder 2 & 1800 & ReLU \\ \hline Output & 2000 & Sigmoid \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Autoencoder architecture details. The first segment of the signal, consisting of 20,000 samples (1 s), is deliberately omitted to disregard the transient response of the system, which leads to overshoot and non-stationarity, as already presented in Figure 3. This decision enhances the results and the framework's detectability. Additionally, white noise is added to the healthy signal to increase the number of healthy signals, prevent overfitting, and improve the framework's robustness to variations. This strategy results in a dataset containing 10 healthy signals (80,000 samples per signal), with each signal divided into segments of 2000 samples (the feature vector size), finally producing a total of 400 healthy feature vectors. The 320 feature vectors (80%) are used for the training of the model and the remaining 80 (20%) for the validation of the model. An identical strategy of white noise addition is employed for the signals under damage, resulting in a dataset of eight signals, each consisting of 6000 samples (0.3 s), which yield 24 feature vectors representing the damage state. Overall, the assessment of the framework is based on 80 healthy and 24 damage inspection cases (104 in total). The outcomes of the damage detection framework are illustrated in the scatter plot in Figure 9. In the scatter plot, a user-defined threshold of 10,000 is depicted with a red dotted line. The distances pertaining to healthy feature vectors, either training (blue color) or validation (green color), are under the threshold, while the feature vectors under damage (red color) result in distances that are over the threshold. This translates into detection accuracy of 100%. In Figure 4, it is worth noting that the interval between the distances of the healthy and damaged states is significant, indicating the high detectability of the damage and clear separability between the healthy and damaged states. Figure 10 shows the confusion matrix, indicating that the model successfully identified only true positives and true negatives, as the results are concentrated along the diagonal. In addition, Table 4 presents, in a quantitative way, the values of different measures for the sensitivity, precision, and accuracy of the framework for the two different values of white noise used: 60 and 78 Eb/No. Table 4 illustrates that the framework operates with high efficiency, even under extreme white noise conditions (W-N = 78 Eb/No), demonstrating a strong capability for the detection of significant damage. Future investigations will involve testing various noise types and analyzing real-world data. However, the impressive performance of the proposed framework prompted a final phase in our research, which entailed comparing its results with an alternative method that has been validated for the same type of generator [22]. This alternative method utilizes a hybrid CNN-wavelet algorithm, where the initial signal is first transformed from the time domain to the frequency-time domain via wavelet analysis, followed by the application of a 2D CNN algorithm to the results of the wavelet analysis  
Figure 9: Scatter plot of the training (blue) and validation (green) distances of healthy feature vectors in comparison with the damage (red) distances. The red dotted line represents the user-defined threshold of value 10,000 (W-N = 60 Eb/No).