## 4 Proposed Method
### Fault Prediction Method
#### 4.3.2 Type of Neural Network Structure  
In general, neural network structures come in many variants, including single-layer, multi-layer, and bidirectional neural network structures. In general, a single-layer neural network has a simple structure and high computational efficiency, but its learning ability is limited. A multi-layer neural network has stronger representation and learning ability, which is suitable for various complex tasks. A bidirectional neural network can make use of bidirectional dependency to provide more comprehensive context information when processing sequence data. Choosing a suitable neural network structure depends on the complexity of the task and the characteristics of the data.  
Several variants of the LSTM structure are shown in Figure 12, including:  
1. Single-layer LSTM: Also known as standard LSTM, it contains three gate control units (input gate, forget gate, and output gate), as well as a memory unit (cell state), which can conduct long-term dependency modeling of sequence data.  
2. Multi-layer LSTM: A network structure composed of multiple LSTM layers. Each LSTM layer can obtain input from the output of the previous layer and increase the complexity and representation of the model by stacking multiple LSTM units.  
3. Bidirectional LSTM: Based on the standard LSTM, the forward and reverse LSTM units are introduced, which can model the sequence data in both forward and backward directions at the same time to capture more comprehensive context information.  
Several variations of the GRU structure are shown in Figure 13, including:  
Figure 12: **(A1)** SLLSTM structure, **(A2)** MLLSTM structure, **(A3)** Bi-LSTM structure.  
1. Single-layer GRU neural network: The single-layer GRU neural network contains only one GRU hidden layer and, compared with LSTM, the GRU has a more simplified structure, reducing a part of the gating unit, and the number of calculations and parameters is lower. Single-layer GRUs show better performance when dealing with simple sequential tasks. The training speed is relatively fast, which is suitable for medium-scale data sets.  
2. Multi-layer GRU neural network: The multi-layer GRU neural network contains multiple GRU hidden layers, and the output of the upper layer serves as the input of the next layer. The multi-layer structure can capture more complex sequence patterns and abstract representations, while increasing the depth and expressiveness of the network. It is suitable for processing more complex sequences, but the complexity of training and parameter adjustment is higher. More computing resources and more data are needed to avoid overfitting.  
3. Bidirectional GRU neural networks: Bidirectional GRU neural networks consider both past and future contextual information. At each time step, the input sequence is passed forward and backward to the two GRU hidden layers, and their outputs are merged. Bidirectional architecture can better capture dependencies and context information in sequence data and is suitable for sequential tasks such as speech recognition, machine translation, etc., but the training time and computing resource consumption are relatively high.  
Choosing a single-layer, multi-layer, or bidirectional neural network architecture requires trade-offs based on the specific task and complexity of the data set, as well as training time and computational resource constraints. The LSTM and GRU neural network structures with an attention mechanism are shown in Figures 14 and 15.