# Fault Prediction for Rotating Mechanism of Satellite Based on SSA and Improved Informer
## 4 Experiments
### Model Learning Process
**TCN-SE**: TCN-SE is a Time Convolutional Network (TCN) with an added attention mechanism. It utilizes the TCN network for feature extraction and employs the SE attention mechanism to allocate weights between feature vectors. Its inherent dilated convolution mechanism allows it to use long-term historical information for prediction by increasing the receptive field without significantly increasing model depth [34]. **Transformer**: The transformer consists of two parts: the encoder and the decoder. The encoder embeds the input sequence, performs feature extraction through multiple layers of self-attention mechanisms, and finally outputs the encoded representation. The decoder generates the output sequence based on the encoded representation using another layer of self-attention and a feedforward neural network [35]. A comparison of the complexities of various models indicates that the computational complexity of CNN-LSTM is primarily constrained by the sequence length and the hidden state dimensions of the LSTM component. This results in a model with moderate complexity, making it suitable for medium-scale data and sequences. DA-RNN, which integrates attention mechanisms with RNNs, significantly increases the number of model parameters. Its computational complexity is relatively high, especially for longer sequences, due to the substantial computational overhead of the attention mechanism. The transformer model's number of parameters depends on the number of layers, attention heads, and hidden dimensions, leading to a large model size. It also exhibits high computational complexity, making it suitable for medium-length sequences and large datasets. Informer has a comparable number of parameters to the standard transformer; however, by utilizing a sparse attention mechanism, it reduces actual computational load and memory consumption, particularly for long sequences and large-scale data. Therefore, overall, the method proposed in this paper has lower complexity compared to other models and can more efficiently handle large-scale data tasks. Additionally, this paper compared the prediction results of our proposed method with those of LightGBM, LSTM, CNN-LSTM, DA-RNN, TCN-SE, transformer, and informer. The MSE, MAE and \(R^{2}\) for the two datasets obtained using different methods are shown in Table 3, with the best results highlighted in bold. The results in the table indicate that our proposed model performs exceptionally well in both evaluation metrics, with accuracy improvements exceeding 10% compared to other algorithms. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MAE** & **MSE** & \(\mathbf{R^{2}}\) \\ \hline LightGBM & 1.225 & 0.296 & 0.704 \\ LSTM & 0.763 & 0.187 & 0.793 \\ CNN-LSTM & 0.657 & 0.176 & 0.816 \\ DA-RNN & 0.781 & 0.202 & 0.815 \\ TCN-SE & 0.658 & 0.159 & 0.834 \\ Transformer & 0.486 & 0.127 & 0.882 \\ Informer & 0.442 & 0.108 & 0.898 \\ Proposed model & \(\mathbf{0.405}\) & \(\mathbf{0.086}\) & \(\mathbf{0.904}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of different models.