# A Novel Integrated Fault Diagnosis Method Based on Digital Twins
## 4 Experiments and Results
### Comparative Experiments
#### 4.2.2 Experimental Setup  
To ensure the validity and comparability of the experimental results, we established the following requirements for the proposed network model and the control network models:  
1. For the control models included in the comparison, parameters and network layers should be as consistent as possible. For instance, for layered models, we set the number of layers to be equal and strived to maintain consistency in other model parameters, as detailed in Table 1.
2. To improve the efficiency of the diagnostic model, we incorporated regularization and Dropout techniques into the network model to prevent overfitting. These measures ensured that the experimental comparison was fair and meaningful, allowing for a clear evaluation of the proposed DCBA fault diagnosis model against traditional and classical deep learning methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Layer** & **Name of Layer** & **Number of Layer** & **Output Shape** & **Number of Parameters** \\ \hline \multirow{4}{*}{LSTM} & Layer1 & LSTM & 5 & (None, 10, 128) & 614,400 \\  & Layer2 & Dropout & 1 & (None, 128) & - \\  & Layer2 & Linear & 1 & (None, 8) & 1032 \\  & & Conv1d & 1 & (None, 3, 10) & 345 \\  & & ReLU & 1 & (None, 3, 10) & - \\  & & Conv1d & 1 & (None, 3, 10) & 30 \\  & & ReLU & 1 & (None, 3, 10) & - \\  & Layer3 & Conv1d & 1 & (None, 3, 10) & 30 \\ CNN & & ReLU & 1 & (None, 3, 10) & - \\  & Layer4 & Conv1d & 1 & (None, 3, 10) & 30 \\  & & ReLU & 1 & (None, 3, 10) & - \\  & & Conv1d & 1 & (None, 3, 10) & 30 \\  & & ReLU & 1 & (None, 3, 10) & - \\  & & AvgPoolide & 1 & (None, 3, 1) & - \\  & & Linear & 1 & (None, 8) & 32 \\  & & Linear & 1 & (None, 128) & 48,768 \\  & & ReLU & 1 & (None, 128) & - \\  & & Linear & 1 & (None, 128) & 16,512 \\  & & ReLU & 1 & (None, 128) & - \\  & & Linear & 1 & (None, 128) & 16,512 \\ ANN & & ReLU & 1 & (None, 128) & - \\  & Layer4 & Linear & 1 & (None, 128) & 16,512 \\  & & ReLU & 1 & (None, 128) & - \\  & & Linear & 1 & (None, 128) & - \\  & & Linear & 1 & (None, 8) & 1032 \\  & & Layer1 & Conv1d & 1 & (None, 64, 10) & 7360 \\ CNN-BLSTM & Layer2 & MaxPoolid & 1 & (None, 64, 5) & - \\  & & BLSTM & 4 & (None, 5, 256) & 989,184 \\  & & Layer3 & Dropout & 1 & (None, 256) & - \\  & & Linear & 1 & (None, 8) & 2056 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detailed settings of comparative experimental model parameters. Specifically, the convolutional layer used a 1D convolutional kernel with a kernel size of 3 to effectively capture local features in the input sequence. This choice of kernel size is not only closely related to the temporal characteristics of the data but also considers the granularity of feature extraction. A smaller kernel size allows for the extraction of low-level features, such as local patterns and short-term dependencies, while maintaining computational efficiency. Additionally, the smaller kernel size helps avoid introducing excessive parameters, reduces computational overhead, and retains sufficient temporal information to some extent. Experimental results show that a kernel size of 3 provided a good balance between feature-capturing ability and computational resources. To further model the long-term dependencies in the temporal data, this paper employed a Bidirectional Long Short-Term Memory (BLSTM) network, which enhanced the model's understanding and predictive power of temporal patterns by considering both forward and backward information. To balance the model's expressive power with computational efficiency, we chose 256 hidden units, which improves the model's learning capacity without imposing too heavy a computational burden. To further enhance model performance, this paper introduced an attention mechanism, utilizing a simple additive attention structure to weigh and sum the output features from the LSTM layer, thereby focusing on the key information. To prevent overfitting, a Dropout layer was incorporated into the network, randomly dropping some neurons to improve the model's generalization ability.