## 5 Conclusions  
This article proposes a CMG anomaly detection method based on working condition classification and transfer learning. A two-stage transfer learning is designed to solve the problem of poor training effect due to the lack of fault data. A Sinc-LSTM network is designed to model each telemetry channel separately, classify the working conditions and set adaptive thresholds for anomaly detection. Experimental results show that transfer learning, working condition classification, and a Sinc filtering layer can improve the performance of CMG anomaly detection tasks, with the improvement of transfer learning being the most significant. Working condition classification is a refined processing method for anomaly detection in long-cycle tasks and is also of great significance. As a key inertial execution component, the operational data of the CMG is representative, and the anomaly detection method for the CMG can also be transferred to other parts of the spacecraft.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & \(P\) **(\%)** & \(R\) **(\%)** & \(A\) **(\%)** & \(F1\) **(\%)** & **Training Time(s)** \\ \hline WCC+TL+Sinc+LSTM (The proposed method) & **93.7** & **89.6** & **96.7** & **91.6** & **24.7** \\ TL+Sinc+LSTM (WCG) & 83.6 & 82.8 & 87.9 & 83.2 & 23.6 \\ WCC+Sinc+LSTM (Ft) & 71.3 & 69.7 & 79.8 & 70.5 & 31.4 \\ WCC+Sinc+LSTM (one-step pre-training) & 91.5 & 88.1 & 94.7 & 89.8 & 27.8 \\ WCC+TL+LSTM (Sine) & 89.3 & 87.4 & 92.9 & 88.3 & 21.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Self contrast experiments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & \(P\) **(\%)** & \(R\) **(\%)** & \(A\) **(\%)** & \(F1\) **(\%)** \\ \hline The proposed method & **93.7** & **89.6** & **96.7** & **91.6** \\ [PERSON] et al. [14] & 75.4 & 63.6 & 82.4 & 69.0 \\ TadGAN [49] & 80.1 & 72.9 & 85.8 & 76.3 \\ StackedPredictor [50] & 83.5 & 76.2 & 91.2 & 79.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with advanced methods.  
In addition, by adjusting the training set, the proposed method can be applied to other time-series data anomaly detection tasks.  
During the training process, the model still has shortcomings, such as slow convergence speed and low training efficiency. Faced with thousands of telemetry data channels commonly used in spacecraft, the time cost of the strategy proposed for training each channel separately is high. To address the problem of large-scale telemetry data and multiple channels in spacecraft, a universal model for spacecraft component anomaly detection with strong generalization ability can be trained by increasing the depth of the model and the size of the training set. Introducing the time-frequency two-dimensional features of one-dimensional signals can enhance the neural network's perception of nonlinear time-varying features. Moreover, using models without temporal dependencies, such as convolutional neural networks, can greatly reduce the time cost of model training. In the future, our research will be based on deep learning, transfer learning, time-frequency analysis, and other methods, involving fault diagnosis of spacecraft multimodal signals and complex systems.  
Methodology, K.Z. and Q.X.; Writing--original draft, S.W. ([PERSON]); Writing--review & editing, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant 61972021.  
Not applicable  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to confidentiality.  
The authors declare no conflict of interest.