## 3 Results
### CNN Configuration with Corrupted Images  
Expanding on the previous methodology, the data set containing images corrupted with 2% salt and pepper noise was utilized. Subsequently, the CNN was trained using various configurations, as outlined in Table 3, which also presents the corresponding accuracy results.  
The results in Table 3 show a decrease compared to those in Table 2. This decrease could potentially be attributed to the incorporation of noise applied to the input images for the purpose of database augmentation. In addition, it has been documented that the lower the number of filters in a convolutional layer, the better the accuracy of the system and the better the execution time of the network training [40]. However, it is important to note that the configuration that yielded the best results aligned with the optimal performance of the CNN in both stages. In the ultimate CNN model, the ReLU activation function, batch normalization, eight filters, a filter size of 3 \(\times\) 3, and a 2 \(\times\) 2 average pooling layer were employed, along with 20 epochs. These parameter values were determined after experimenting with various configurations. Figure 7 visually represents the accuracy (Figure 7a) and loss (Figure 7b) curves observed during the training process.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Pooling Layer**} & \multirow{2}{*}{**Epochs**} & \multicolumn{2}{c}{**8 Filters**} & \multicolumn{2}{c}{**16 Filters**} \\ \cline{3-6}  & & \(\mathbf{3\times 3}\) & \(\mathbf{4\times 4}\) & \(\mathbf{3\times 3}\) & \(\mathbf{4\times 4}\) \\ \hline Average & 10 & 79.93\% & 82.94\% & 59.24\% & 44.19\% \\ Pooling & 20 & **97.81\%** & 89.91\% & 68.03\% & 61.58\% \\ \hline Maximum & 10 & 38.77\% & 17.96\% & 31.51\% & 22.78\% \\ Pooling & 20 & 69.58\% & 36.82\% & 49.08\% & 55.04\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Configuration of the CNN model using corrupted images.  
Figure 6: Confusion matrix of CNN model with horizontal flip images.  
The confusion matrix was derived to quantify the predictions conducted during the validation of the trained CNN model. The number of input images accounted for 20% of the entire database, totaling 960 images. This equates to 80 images for each IM and gearbox condition. The metrics of accuracy = 97.80%, precision = 97.82%, recall = 97.80%, and the F1-Score = 97.75% were extracted from the data depicted in Figure 8.  
Figure 8: Confusion matrix of CNN model with corrupted images.  
Figure 7: Training process of CNN with corrupted images.  
The results in Table 2 show a clear improvement over those in Table 3, indicating that the introduction of noise has a detrimental effect on performance. In both cases, the optimal configuration consisted of eight filters, a 3 \(\times\) 3 filter size, a pooling average, and 20 epochs. The accuracy dropped from 98.54% (Table 2) to 97.81% (Table 3). For other configurations, such as 16-filter pooling and a 4 \(\times\) 4 size, the decrease was much more pronounced: 62.29% (Table 2) versus 55.04% (Table 3). The decrease in accuracy in Table 3 can be attributed to the difficulty of the model to generalize when data with artificial perturbations (noise) are introduced. This suggests that while noise may improve the robustness of the model in real-world scenarios, it also introduces additional complexity during training. Despite the reduced performance observed in Table 3 due to noise, it is important to recognize that this may more accurately reflect real-world operational conditions. The discrepancies highlight the need to strike a balance between accuracy and robustness tailored to the specific environment in which the proposed methodology is deployed.