# Fault Prediction for Rotating Mechanism of Satellite Based on SSA and Improved Informer
## 4 Experiments  
### Dataset Processing and SSA Procedure  
In order to accurately predict the health status of a satellite's rotating mechanism, this paper utilize the telemetry data collected from a satellite's 30-day turntable operation as our dataset. The data used in this study were sourced from the telemetry system of an in-orbit satellite. The data collection frequency varies depending on the operating mode, with a maximum frequency of 1 Hz and a minimum of 1/64 Hz. The dataset includes two primary variables: motor current and turntable temperature. The motor current reflects the current consumed by the motor during different phases of turntable operation, providing critical information about motor load and performance. The turntable temperature data are used to monitor temperature variations, helping to assess the thermal state of the equipment and its operational safety. Furthermore, due to the limited availability of satellite telemetry data, these two types of data are among the few that can directly reflect the state of the rotational mechanisms. The dataset consists of 200,000 records covering a 30-day period, providing a rich foundation for subsequent analysis and modeling. These data volumes are sufficient not only for basic statistical analysis but also for training and validating more complex time-series forecasting models and anomaly detection algorithms. In this study, the dataset will be used for an in-depth analysis to identify potential patterns, trends, and anomalies, thereby providing empirical support for the satellite turntable's health monitoring and fault prediction system. Figure 4 presents the variation curves of the turntable temperature and motor current data over a one-day period. The raw telemetry data from the satellite turntable subsystem are normalized using min-max normalization to map the values into the [0,1] range. This step is crucial for ensuring that the data are on a comparable scale, which aids in improving the performance of the subsequent analysis. The normalization formula is  
\[\text{Normalized\_data}=\frac{\text{data}-\text{min(data)}}{\text{max(data)}- \text{min(data)}} \tag{15}\]  
The dataset spans a considerable period and has a sampling frequency of 1/16 Hz, adequately capturing the state changes of the satellite turntable during its operation. After preliminary data analysis, a time series representing the normal operation of the satellite rotation mechanism was selected for modeling. A 7-day period of turntable motor current data and turntable temperature data under normal operating conditions was used to train and generate a health model for the satellite rotation mechanism's temperature. To evaluate the accuracy of predictions and to facilitate comparison, this paper applies three classical evaluation indexes, which are mean absolute error (MAE), mean square error (MSE) and R\({}^{2}\) score defined in Formulas (16)-(18), respectively. \[MAE=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-g_{i}| \tag{16}\]  
Figure 4: Diagram of motor current and turntable temperature data. \[MSE=\frac{1}{n}\sum_{i=1}^{n}{(y_{i}-\bar{y}_{i})^{2}} \tag{17}\] \[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\bar{y}_{i})^{2}}{\sum_{i=1}^{n} (y_{i}-\bar{y})^{2}} \tag{18}\]  
where \(y_{i}\) is the actual value, \(\bar{y}_{i}\) is the predicted value, \(\bar{y}\) is the mean of the actual values, and \(n\) is the number of data points. Since the SSA-CNN-Informer model combines the SSA algorithm with an improved informer model, the parameter settings for this model need to be analyzed in two steps. First, the parameters for the SSA algorithm must be configured. As shown in Table 1, to ensure the effectiveness of the SSA algorithm, the length of the time series used for training must be greater than the number of decomposed sequences. For the turntable motor current data and turntable temperature data used in this study, we chose to decompose the data into five sub-sequences. Through preliminary experiments, the window length was fixed at 120, and the reconstruction order was set to 3.