## 5 Results and Discussion
The scores in Table 4 clearly show that the proposed model provides superior performance to TD-LSTM and BiGRU. Recall calculated the model's ability to identify all relevant instances by calculating the proportion of the true positive predictions of all of the actual positive instances. F1 score combined precision and recall into a single metric by calculating their harmonic mean. Similarly to the accuracy and precision results, the proposed model showed better recall and F1 scores than the comparison models, as presented in Figure 8:3  
The proposed model performed better than the comparison models for several reasons. Firstly, the proposed method provides effective feature extraction. The CNN layers in the proposed model are well suited to extracting relevant features from AE signals. Convolutional layers are adept at capturing local pattern features from the signal, which plays a key role in handling time series data such as AE signals. After extraction of the features through the CNN layers, the LSTM layers effectively capture the temporal dependencies and sequence information. This combination allows the model to benefit from both the spatial and temporal aspects of the data, leading to better performance in terms of classification. The proposed model integrates feature extraction and sequence modeling into a single end-to-end framework, which can lead to more efficient learning and better performance compared to the models that use separate branches, such as TD-LSTM. TD-LSTM did not perform well compared to the proposed model because its combining of time domain features with LSTM-extracted features might not always lead to improved performance if the time domain features do not effectively complement the LSTM features. Time domain features may not capture the relevant local patterns as effectively as the features learned by the CNN-LSTM model, leading to low performance. If the time domain features are not well aligned with the sequence information, their integration with \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Dataset** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{6}{*}{Proposed} & Dataset 1 & 99.81\% & 99.92\% & 99.84\% & 99.87\% \\  & Dataset 2 & 99.93\% & 99.86\% & 99.95\% & 99.90\% \\  & Dataset 3 & 99.84\% & 99.79\% & 99.87\% & 99.82\% \\  & Dataset 4 & 99.87\% & 99.84\% & 99.91\% & 99.87\% \\ \cline{2-6}  & Average scores & 99.86\% & 99.85\% & 99.89\% & 99.85\% \\ \hline \multirow{6}{*}{TD-LSTM} & Dataset 1 & 97.78\% & 98.21\% & 97.22\% & 97.71\% \\  & Dataset 2 & 98.89\% & 99.09\% & 98.61\% & 98.84\% \\ \cline{1-1}  & Dataset 3 & 75.56\% & 81.76\% & 71.92\% & 76.52\% \\ \cline{1-1}  & Dataset 4 & 97.28\% & 98.25\% & 97.29\% & 97.76\% \\ \cline{1-1} \cline{2-6}  & Average scores & 92.37\% & 94.32\% & 91.26\% & 92.71\% \\ \hline \multirow{6}{*}{Bi GRU} & Dataset 1 & 98.54\% & 98.31\% & 98.42\% & 98.36\% \\  & Dataset 2 & 99.05\% & 98.89\% & 99.11\% & 98.99\% \\ \cline{1-1}  & Dataset 3 & 98.74\% & 98.59\% & 98.78\% & 98.68\% \\ \cline{1-1}  & Dataset 4 & 98.68\% & 98.55\% & 98.69\% & 98.62\% \\ \cline{1-1} \cline{2-6}  & Average scores & 98.75\% & 98.58\% & 98.74\% & 98.66\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Scores. the LSTM features may not confer additional benefits. In the case of BiGRU, although it performed better than TD-LSTM, its performance was not as effective as the proposed model. A possible reason is that the BiGRU model lacks dedicated feature extraction layers. It processes raw sequences directly, which might not be as effective for complex AE signals that require sophisticated feature extraction to improve classification accuracy. The computational complexity of the proposed model, which combines CNN for spatial feature extraction and LSTM for temporal sequence analysis, is slightly higher than the comparison models, namely TD-LSTM and BIGRU.