## 4 Model Verification and Comparison Based on 1 DCNN-BiLSTM-AdaBoost
### Algorithm Comparison and Verification  
To validate the superiority of the 1 DCNN-BiLSTM-AdaBoost model, we conducted performance experiments comparing it with the five other models listed in Table 5. The results, presented in Figure 8 and Table 5, highlight the performance differences across multiple evaluation metrics, such as accuracy, recall, and F1-score. These comparisons clearly demonstrate that the proposed 1 DCNN-BiLSTM-AdaBoost model significantly outperforms the other models in various aspects. First, among the single-model comparisons, the 1 DCNN model achieved the best performance within its category, with consistently high values across all evaluation metrics, indicating its effectiveness in processing time-series data. Second, the BiLSTM model improved accuracy by 5.37% compared to the traditional LSTM model, demonstrating that the bidirectional LSTM structure is more effective in capturing long- and short-term dependencies in time-series data. Furthermore, when using a single data source, the 1 DCNN-BiLSTM model improved accuracy by 6.10% over the standalone 1 DCNN model, proving that the information fusion strategy effectively enhances diagnostic capabilities by integrating sensor data from different modalities. When incorporating dual-channel data fusion, the performance of the 1 DCNN-BiLSTM model improved even further, highlighting the advantage of leveraging multiple data sources. Finally, after integrating the AdaBoost algorithm with the 1 DCNN-BiLSTM model, all evaluation metrics improved by approximately 2-3%, confirming the effectiveness of ensemble learning in optimizing performance. The AdaBoost-enhanced model prioritizes misclassified samples, focusing on the model's weaknesses during training, thereby improving overall accuracy and robustness.  
In summary, the proposed 1 DCNN-BiLSTM-AdaBoost model demonstrates clear advantages across all evaluation metrics, proving its high accuracy and robustness in wind turbine-blade fault detection. The key findings of this study can be summarized as follows: (1) BiLSTM outperforms traditional LSTM in capturing temporal dependencies; (2) 1 DCNN effectively extracts local features; (3) Combining 1 DCNN with BiLSTM enables the model to capture both local and global information, further enhancing prediction accuracy; (4) Multi-channel data fusion significantly improves overall model performance; (5) The integration of the AdaBoost ensemble strategy enhances weak classifiers, allowing the final model to achieve optimal performance in wind turbine blade fault detection tasks.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Metric 1** & **Metric 2** & **Metric 3** & **Metric 4** \\ \hline LSTM & 0.6427 & 0.5661 & 0.6429 & 0.5673 \\ BiLSTM & 0.6964 & 0.5938 & 0.6967 & 0.6130 \\
1D CNN & 0.7400 & 0.6250 & 0.7400 & 0.6667 \\
1D CNN-BiLSTM (Single-Channel) & 0.7800 & 0.6450 & 0.7791 & 0.6863 \\
1D CNN-BiLSTM (Dual-Channel) & 0.9375 & 0.9500 & 0.9377 & 0.9395 \\ Proposed Method (This Paper) & 0.9688 & 0.9722 & 0.9692 & 0.9686 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model performance comparison.