# A Novel Integrated Fault Diagnosis Method Based on Digital Twins
## 4 Experiments and Results
### Comparative Experiments
#### 4.2.4 Results and Analysis
This can be explained by the fact that the DCBA model not only integrates the sequential feature extraction capability of LSTM but also leverages the spatial feature extraction advantage of CNN and the key feature capture ability of the attention module, thereby providing more comprehensive fault diagnosis performance  
Furthermore, in terms of NS (noise sensitivity), the proposed DCBA model demonstrates significant advantages over traditional deep learning models. Although slightly inferior to ANN in NS, which may be due to the complexity of the DCBA network, observations show that DCBA outperforms ANN in PA. This suggests that while the DCBA model is complex in some aspects, its overall performance and specific metrics are superior. This further indicates that integrating feature information and temporal information helps enhance the model's diagnostic effectiveness and sensitivity to noise. Table 4 displays the comparison of various models in terms of Precision, Recall, and F1 score, demonstrating that the proposed DCBA model outperforms traditional deep learning and machine learning models in these metrics. These results indicate significant advantages of the DCBA model in Precision, Recall, and F1 score, highlighting its superiority in comprehensive performance evaluation. Experimental results demonstrate that the proposed DCBA model achieves outstanding performance across multiple metrics, including PA, NS, Precision, Recall, and F1 score, fully validating its effectiveness and potential in the field of equipment fault diagnosis. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Index** & **DCBA** & **LSTM** & **CNN** & **CNN-BLSTM** & **Transformer** & **ANN** & **SVM** \\ \hline Precision & 0.968 & 0.965 & 0.882 & 0.963 & 0.964 & 0.953 & 0.965 \\ Recall & 0.964 & 0.959 & 0.866 & 0.963 & 0.963 & 0.948 & 0.960 \\ F1 score & 0.964 & 0.959 & 0.869 & 0.963 & 0.963 & 0.947 & 0.959 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of Precision, Recall, and F1 score across models. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Index** & **DCBA** & **LSTM** & **CNN** & **CNN-BLSTM** & **Transformer** & **ANN** & **SVM** \\ \hline \(PA_{normal}\) & 0.9646 & 0.9592 & 0.8662 & 0.9627 & 0.9632 & 0.9479 & 0.9601 \\ \(PA_{noise}\) & 0.9171 & 0.9094 & 0.6797 & 0.9119 & 0.9036 & 0.9057 & 0.8937 \\ \(AP_{A}\) & 0.0475 & 0.0498 & 0.1865 & 0.0508 & 0.0596 & 0.0422 & 0.0664 \\ NS & 4.92 & 5.19 & 21.53 & 5.28 & 6.18 & 4.45 & 6.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: PA of models with noisy and noiseless data. Although the performance improvement of the DCBA model over LSTM, CNN-BiLSTM, Transformer, and SVM in certain metrics is relatively modest, the DCBA model exhibits distinct advantages in noise robustness and complex feature extraction. Specifically, the DCBA model demonstrates minimal performance fluctuation in noisy environments, indicating its superior robustness and stability, which enhances its diagnostic reliability under complex working conditions. Furthermore, the DCBA model effectively integrates CNN's spatial feature extraction capability, BLSTM's temporal sequence modeling strength, and the attention mechanism's key feature focusing ability. This comprehensive design enables improved feature extraction and fusion, significantly enhancing the model's performance in complex pattern recognition tasks. Such characteristics further reinforce the DCBA model's practicality and reliability for real-world industrial applications.