# Fault Prediction for Rotating Mechanism of Satellite Based on SSA and Improved Informer
## 4 Experiments
### Model Learning Process  
The rationale for employing a hybrid model is to leverage the strengths of multiple models, thereby enhancing the robustness of the model against diverse data distributions. This is particularly crucial in low-dimensional feature spaces, where a single model may fail to capture all underlying patterns. The CNN-Informer model obtains correlations within and between data windows through overall training of the window. The prediction length can be set to different values but must be less than the training window length. A single forward prediction is performed through the window. First, by applying the previously described data normalization method, we standardize the newly generated motor current data (denoised using singular spectrum analysis, SSA) along with the turntable temperature data. This ensures that both datasets are distributed within the reasonable range of [0,1]  
Figure 8: SSA reconstruction sequence of temperature and raw sequence. Figure 7: SSA decomposition sub-sequence of turntable temperature. and converted into data tensors. The next step is to add positional information within the attention window, which captures data correlations in the dataset. Finally, the Adam optimizer is used to optimize the model parameters. The Adam optimizer is a gradient descent algorithm with adaptive learning rates that can handle sparse gradients and mitigate gradient oscillation issues. The model parameter settings are shown in Table 2. As illustrated in Figure 9, with continued training and parameter adjustments, the training error rapidly decreases and stabilizes. The model trained with the parameters listed in Table 2 was used to make predictions on the test set, focusing on forecasting turntable temperature data for 600 time steps. The visualized results of the model's predictions are shown in Figure 10. To further validate the performance of the proposed model in this study, we selected several popular models for multi-step time-series prediction in recent years:  
**LightGBM**: Light Gradient Boosting Machine (LightGBM) is an efficient gradient boosting framework, particularly suitable for large-scale data and high-dimensional features. Compared to traditional gradient boosting algorithms, LightGBM demonstrates significant advantages in speed and memory usage, making it highly suitable for tasks such as fault prediction. The complexity of the model can be controlled by adjusting parameters such as tree depth, number of leaves, and learning rate. In comparison to deep learning models, it offers higher training efficiency [31]. **LSTM**: Long Short-Term Memory is a type of recurrent neural network that can learn long-term dependencies. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Model Parameter Setting**} \\ \hline num of convolution kernel & 8 \\ input sequence length of encoder & 512 \\ prediction sequence length & 120 \\ num of encoder layers & 2 \\ num of decoder layers & 1 \\ dropout & 0.05 \\ optimizer learning rate & 0.001 \\ activation function & relu \\ optimizer & Adam \\ loss function & MSE \\ batch size of train input data & 22 \\ epoch & 80 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The value of model parameter. Figure 9: Model loss curve. **CNN-LSTM**: The CNN-LSTM model combines Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, making it suitable for handling sequence data that integrate spatial and temporal information. This model can extract spatial features and temporal dependencies in time-series data, making it applicable to various time-series prediction tasks [32]. **DA-RNN**: Dual-Stage Attention-Based Recurrent Neural Network (DA-RNN) is a time-series prediction model based on an encoder-decoder structure that utilizes an input attention mechanism to select input features and a temporal attention mechanism to select hidden layer features. By incorporating these dual attention mechanisms, DA-RNN effectively selects feature vectors and establishes long-term historical information dependencies [33].