# Fault Prediction for Rotating Mechanism of Satellite Based on SSA and Improved Informer
###### Abstract
Some researchers in the satellite field have attempted to combine neural networks with statistical methods or model-based methods [13]. While statistical and shallow learning-based fault diagnosis methods have made significant advancements over traditional fault diagnosis algorithms, these methods often require domain-specific knowledge to extract fault features, and their recognition accuracy still needs improvement. In recent years, the rapid development of machine learning and artificial intelligence has made deep learning a popular topic. Deep learning, characterized by network structures with multiple hidden layers, has excellent feature extraction and learning capabilities. It can effectively analyze and extract hidden feature information from large amounts of data, establishing mapping relationships between data and features. Deep learning algorithms overcome many of the inconveniences of traditional algorithms and have been successfully applied in fields such as image, text, and video analysis [12]. Gradually, many researchers have attempted to use predictive methods for fault detection. [PERSON] et al. [14] employed Gaussian Process Regression to detect sensor anomalies in aircraft engines, demonstrating good predictive performance. [PERSON] et al. used the Extended Kalman Filter method for fault diagnosis and isolation in spacecraft attitude control systems [15]. The advantage of this method lies in its ability to estimate both the system state and the uncertainty of the state. Other researchers have used system identification methods to predict target variables. [PERSON] et al. [16] proposed a model combining wavelet approximation with system identification theory for anomaly detection in network signals. [PERSON] et al. [17] utilized an Autoregressive with Exogenous Inputs (ARX) model for anomaly detection in wireless communication network data systems. [PERSON] et al. [18] combined neural networks with the Hammerstein-Wiener system identification model to estimate the state of a quadrotor system and establish a set of residuals to detect sensor anomalies. AutoEncoder, a type of artificial neural network, can learn effective representations of input data through unsupervised learning, thus performing data dimensionality reduction. [PERSON] et al. [19] used deep autoencoders for anomaly detection. However, relevant studies seldom address telemetry data similar to those of in-orbit satellites, nor do they consider seasonal trends, periodic trends, noise, and the limitations of on-board real data sampling frequency [20]. Additionally, most of these methods simply stack different networks sequentially, which can lead to some degree of feature loss when further extracting features across different networks. Therefore, although these algorithms may show performance improvements compared to previous algorithms, they still require further refinement due to the potential loss of information. A novel approach for fault prediction in space rotational mechanisms based on telemetry data is proposed. By analyzing the causes, manifestations, and influencing factors of rotational mechanism faults, a hybrid signal processing method combined with a self-attention network for fault prediction is introduced. This approach is better suited to addressing practical engineering issues, such as the operational states of satellite rotational mechanisms under special working conditions and the potential noise interference during telemetry data transmission. This paper proposes a fault prediction method for satellite rotation mechanisms, integrating singular spectrum analysis (SSA) and an improved informer model. Telemetry data from the satellite turntable subsystem were analyzed. First, min-max normalization mapped the data to the [0,1] range. SSA was then used for data decomposition to enhance prediction accuracy. The dataset was trained using the optimized informer model, and key parameter impacts on prediction were evaluated. Through experiments, the best parameter combination was identified to maximize CNN-Informer performance.