## 4 Experiments
### Self Contrast Experiments  
In order to verify the effectiveness of each step of the proposed method, self-comparison experiments are set up. One step of the proposed method is omitted at a time, and then the experimental results are compared with those of the complete method. In this subsection, we will first introduce the treatment method after each step is deleted, and then show the experimental results. First, the experimental group with the working condition classification step was deleted. In the working condition classification stage, each telemetry channel is segmented and clustered separately, and then the telemetry data file is made according to the clustering results of the channel to be predicted. After the telemetry data files are made, they are divided into different batches according to different working conditions to input into the network for piecewise reconstruction of telemetry data. After deleting the condition classification step, the data of the whole period is segmented without clustering and directly input into the network for training. At the same time, a constant threshold is set for anomaly detection based on the full-period data. After canceling the adaptive threshold judgment of the working condition classification, the abnormal false detection rate will be greatly increased because the threshold is not adaptive to each working condition stage. Then is the experimental group with deletion of transfer learning step. This step contains two control groups. One group is the result of no pre-training, and the initial weight of the network is randomly generated, and the other group is the result of one-stage pre-training using only the MRO dataset. The experimental group without pre-training will greatly decrease the convergence speed during the training of the CMG, so the maximum number of training times is set to 100 epochs. In the control group without transfer learning pre-training, the generalization ability will be greatly reduced in addition to the training speed. Due to the characteristics of fault data, it is difficult to obtain the annotated fault data. The model does not converge easily due to the small size of the CMG dataset, and too many iterations tend to cause overfitting. It is beneficial to reduce the overfitting of the network and enhance the generalization ability of the model by extracting the universal features of the spacecraft time series data into the network by pre-training. The precision and recall of anomaly detection in the control group with deletion of the transfer learning step will be greatly reduced. One stage of transfer learning can greatly improve the performance of the network. Finally, the experimental group with the Sinc function filter layer was deleted. Sinc function filter can well fit the characteristics of one-dimensional temporal data due to its interpretability and special band-pass filter structure. Setting the Sinc filter layer at the input end of the network is helpful in improving the feature extraction ability of the network for one-dimensional temporal data. In this paper, six Sinc filters with different bandwidths are set to extract features from the network. In the training process, the number of parameters can be reduced, and the training efficiency can also be improved. The ability of network fitting to reconstruct time series data with the deletion of the Sinc filter layer is affected. The accuracy of the tests and the recall rate have decreased. **Experimental results and analysis. ** Experimental renderings are shown in Figure 5, with original data and reconstructed sequences on the left and difference sequences and detected abnormal sequences on the right. Abnormal detection results are shown in Table 2. It can be seen that the precision and recall rate of the proposed method have reached the highest. In the control group, the performance of the control group with the deletion of transfer learning step decreased the most, the accuracy and recall decreased by 22.2% and 19.9%, respectively.