## 3 Results of the Model  
### Fault Classification  
In this section, the classification outcomes of the proposed model, which was independently trained using all three datasets, are presented. 1. Results of the public dataset  
Figure 8a,b illustrates the graphs depicting the changes in accuracy and loss, respectively, during the training stage of the proposed model using the public dataset. It has been observed that as the number of epochs increases in the training process, the accuracy value gradually increases. On the other hand, the loss curve of the machine learning process shows that as the number of repetitions of the machine learning data increases, the loss function value gradually decreases. Figure 8: (**a**) Accuracy performance of the proposed model using the public dataset. (**b**) The loss of the proposed model using the public dataset. After the training stage of the model, the confusion matrix was calculated to determine the performance of the proposed model, and scores of different performance criteria using confusion matrix are presented in a table in Figure 9a with a classification report. Micro averages (avg) are useful when the classes are imbalanced, and it is important to have a better understanding of the model's performance on the majority class. Therefore, it was observed that the micro avg values were 0.98. ROC curves provide a comprehensive overview of the performance of a classification model across different threshold values. They plot the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. This allows for a visual comparison of the trade-offs between sensitivity and specificity. For these reasons, ROC curves were drawn for each class, and the resulting changes are shown in Figure 9b. As seen in the figure, the AUC value obtained for all classes was approximately 1.0. (b) Results of the dataset generated based on Simulink  
The generated Simulink dataset was utilized during the training stage to evaluate the proposed model. The variations in accuracy and loss parameters that were observed are illustrated in Figures 10a and 10b, respectively. The presence of patterns with similar characteristics in the validation set caused the patterns to be classified incorrectly. In addition, this situation caused a sudden drop in the accuracy graph and a sudden rise in the loss graph, as can be seen in Figure 10. As noted in the public dataset, it has been observed that as the number of epochs increases in the training process, the accuracy value gradually increases. However, when compared to the public dataset, it can be seen that high accuracy can be achieved with fewer epochs. The loss curve indicates that the loss function value is reduced with machine learning data repetitions. Figure 9: (**a**) Confusion matrix of the public dataset with a classification report. (**b**) ROC curve of the public dataset. After the training stage of the model, the confusion matrix with a classification report was calculated to determine the performance of the proposed model depicted in Figure 11a. It was observed that the micro avg values were 1.0. ROC curves were drawn for each class, and the resulting changes are shown in Figure 11b. As seen in the figure, the AUC value obtained for all classes was approximately 1.0. Figure 11: (**a**) Confusion matrix of the Gediz dataset with a classification report. (**b**) ROC curve of the real dataset. Figure 10: (**a**) Accuracy performance of the proposed model for the Simulink dataset. (**b**) The loss of proposed model for the Simulink dataset. (c) Results of the real (Gediz) dataset  
In the training stage, the real dataset was utilized in the proposed model, and Figure 12a,b illustrates the changes that were noticed in terms of accuracy and loss parameters, respectively. As noted in the public and Simulink datasets, it has been observed that as the number of epochs increases in the training process, the accuracy value gradually increases.