## 4 Experimental Results
### Comprehensive Performance Analysis of the Proposed Method  
Figure 8 illustrates the performance comparison among the FC-TL model, the non-TL model, and the proposed model as the dataset size decreases progressively from 100% to 1%. The FC-TL model employs transfer learning by pre-training individual models for different domains during Stage 1, as shown in Figure 4. In this stage, domain-specific features are learned independently for each domain. After training, the classifiers of the domain-specific models are removed, and feature fusion is performed to combine the extracted features from all domains. Following this step, a new classifier is added to utilize the fused features. However, the FC-TL model lacks the bidirectional LSTM and attention mechanism, which are crucial to Stage 2 of the proposed architecture, as depicted in Figure 4. While the FC-TL model effectively uses pre-trained features and benefits from transfer learning and feature fusion, its performance diminishes as the dataset size decreases. Specifically, with approximately 1% of the dataset, the FC-TL model's accuracy drops to 83.45%. While it benefits from TL and feature fusion, the absence of mechanisms to process temporal dependencies or selectively emphasize important features limits its capability, exposing its limitations in achieving robust performance under data-scar scenarios.  
The non-TL model is trained end-to-end in Stage 2 without utilizing Stage 1 pre-training, as shown in Figure 4. Unlike the FC-TL model, it does not utilize transfer learning or pre-trained features from other domains, relying solely on domain-specific features extracted during the training process. When the dataset size was large, the non-TL model performed comparably to the FC-TL model, maintaining stable accuracy. However, as the dataset size decreased, particularly below 30%, its performance declined rapidly. This significant drop demonstrates the heavy reliance of the model on a large volume of labeled data for effective learning and its limited ability to generalize in data-scar conditions.  
In contrast, the proposed model outperformed both the FC-TL and non-TL models, demonstrating exceptional robustness across all dataset sizes. Even with approximately 1% of the data, the proposed model achieved an impressive accuracy of 92.21%, far surpassing the other models. This superior performance is attributed to its efficient utilization of pre-trained features and its ability to combine diverse data through feature fusion. Furthermore, the integration of a bidirectional LSTM enables the model to process temporal information in both forward and backward directions, while the attention mechanism effectively focuses on critical features. These advanced components allow the model to differentiate leak signals from noise with high precision. These advanced components have been experimentally verified, demonstrating that the proposed model can effectively generalize and maintain consistent performance, even with minimal data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Accuracy (\%)** & **F1-Score** \\ \hline MLP-Ensemble & 80.18 & 75.81 \\ SVM-Ensemble & 85.36 & 83.95 \\ LSTM-Ensemble & 74.47 & 66.61 \\
1D CNN-RMS (Only Stage 1) & 98.42 & 98.31 \\
1D CNN-Freq (Only Stage 1) & 98.97 & 98.92 \\
1D CNN-Ensemble & 99.32 & 99.33 \\ Proposed Methods & 99.99 & 99.99 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative analysis of accuracy in detecting low-level pipe leak.