# A Novel Integrated Fault Diagnosis Method Based on Digital Twins
## 4 Experiments and Results
### Fault Mode Analysis  
To gain a clearer understanding of the operational mechanism of the DCBA model, we visualized the output of each layer to observe the fault patterns learned by the model. For this purpose, we selected two different fault samples from the test set, used the trained DCBA model for prediction, and visualized the outputs of each layer in the model, as shown in Figures 9 and 10.  
Specifically, in Figure 9, we selected the 100,000 th and 250,000 th time steps of the equipment operating under the same fault condition for prediction, and the results showed that the predictions were consistent. In contrast, in Figure 10, we selected the 100,000 th time step of the equipment operating under different fault conditions for prediction, and the results showed significant differences. This indicates that the model can effectively distinguish between different fault conditions.  
Based on the information reflected in the figures, we observe the following:  
1. After feature extraction by the CNN layer, significant features are clearly visible. This feature extraction is well demonstrated in the comparison between Figures 9 and 10. The extracted features indicate a high correlation between the features captured by the CNN layer and the final fault diagnosis results. These features help the diagnostic system accurately identify and classify different types of faults, thereby enhancing diagnostic accuracy and reliability.
2. The BLSTM layer further fits and models the feature data extracted by CNN. From the comparison analysis of Figures 9 and 10, we can observe that in Figure 9, the curves show very similar trends and distributions, whereas in Figure 10, although the distributions of the curves are similar, there are differences in trends. This phenomenon indicates that BLSTM not only utilizes the features extracted by CNN but also leverages its advantage in capturing temporal features. These temporal features provide the diagnostic system with more comprehensive and detailed fault information, making the fault diagnosis process more reliable and efficient.
3. The attention layer further processes the integrated feature data from the BLSTM layer. Based on the comparison analysis of Figures 9 and 10, we can clearly observe the following: in Figure 9, there is a high similarity in data information, whereas Figure 10 shows significant differences. This demonstrates that the attention layer effectively extracts crucial information from the input features during the diagnostic process. These key pieces of information may include critical patterns, anomalous changes related to fault types, or other important features, providing decisive support and explanatory capability for the final fault diagnosis results.
4. The linear layer integrates the output from the attention layer. By observing Figures 9 and 10, we conclude the following: in Figure 9, data from different time points under the same fault mode are diagnosed as the same class label data, whereas in Figure 10, data from the same time points under different fault modes are diagnosed as different class label data. This observation validates the effectiveness and practicality of the proposed model in identifying faults.  
Figure 9: A visualization of different temporal samples with the same fault.