# Fault Prediction for Rotating Mechanism of Satellite Based on SSA and Improved Informer
###### Abstract  
The rotational mechanism, which plays a critical role in energy supply, payload antenna pointing, and attitude stabilization in satellites is essential for the overall functionality and performance stability of the satellite. This paper takes the space turntable of a specific satellite model as an example, utilizing high-frequency and high-dimensional telemetry data. An improved informer model is used to predict and diagnose features related to the turntable's operational health, including temperature, rotational speed, and current. In this paper, we present a forecasting method for turntable temperature data using a hybrid model that combines singular spectrum analysis with an enhanced informer model (SSA-Informer), comparing the results with threshold limits to determine if faults occur in the satellite's rotational mechanism. First, during telemetry data processing, singular spectrum analysis (SSA) is proposed to retain the long-term and oscillatory trends in the original data while filtering out noise from interference. Next, the improved informer model predicts the turntable temperature based on the mapping relationship between the turntable subsystem's motor current and temperature, with multiple experiments conducted to obtain optimal parameters. Finally, temperature thresholds generated from the prediction results are used to forecast faults in the rotational mechanism over different time periods. The proposed method is compared with current popular time-series prediction models. The experimental results show that the model achieves high prediction accuracy, with reductions of at least 10% in both the MAE and MSE than CNN-LSTM, DA-RNN, TCN-SE and informer, demonstrating the outstanding advantages of the SSA and improved informer-based method in predicting temperature faults in satellite rotational mechanisms. rotation mechanism; SSA; informer; time-series prediction +
Footnote †: journal: Medical  
and signals. This ensures the satellite's stability during flight. However, the complexity of the rotation subsystem and the harsh operating environment pose significant fault risks. Such faults can compromise the satellite's stability, affecting both mission success and operational lifespan. To enhance the reliability and performance of satellite rotation mechanisms, significant progress has been made in the research of satellite fault diagnosis and prediction. Fault diagnosis techniques can be broadly categorized into knowledge-based methods, model-based methods, and data-driven methods. Knowledge-based methods are relatively difficult to implement and have not been widely adopted. [PERSON] et al. [3] proposed a model-based fault diagnosis method. However, this approach is computationally complex and challenging to model, especially in the context of big data, where its shortcomings are further amplified. Consequently, data-driven fault diagnosis techniques have become the most widely applied methods today. [PERSON] et al. [4] introduced a Just-In-Time Learning Data-Driven (JITL-DD) fault detection method. [PERSON] et al. [5] proposed an improved Probabilistic Principal Component Analysis (PPCA) nonlinear data-driven process monitoring method. With the further development of data-driven fault diagnosis techniques, they can be broadly divided into statistical methods, shallow learning, and deep learning. Statistical-based fault diagnosis techniques include Qualitative Trend Analysis (QTA) [6], Principal Component Analysis (PCA) [7], and Partial Least Squares (PLS) [8], among others. These methods eliminate the complex modeling processes of traditional methods and require minimal prior knowledge. Shallow learning fault diagnosis methods include Support Vector Machines (SVMs) [9], k-Nearest Neighbors (kNNs) [10], Gaussian Mixture Models (GMMs) [11], Artificial Neural Networks (ANNs) [12], and similar techniques. Corresponding hybrid improvement methods also demonstrate good performance.