## 1 Introduction  
In the field of research on satellite fault diagnosis and health management, the real-time health assessment of satellite components is a crucial task to ensure the reliability and operational stability of satellites. Conducting in-orbit health assessment studies for satellite components is significant for various purposes, including fault prediction, performance diagnosis, resource utilization, and data-driven decision making. With the development of the space industry and the increasing complexity of satellite missions, the demand for accurate assessment and real-time monitoring of satellite component health is becoming increasingly pressing [1, 2]. As a result, efficient and reliable in-orbit health assessment of satellite components has become a prominent research topic. Traditional component health assessment methods rely on expert experience and predefined rules. They also have problems such as high subjectivity and poor scalability. Meanwhile, it is difficult to establish an accurate mechanism model due to the complex structure of satellite components and the high uncertainty of space environment changes [3, 4]. Therefore, accurate component health assessment by means of theoretical modeling is difficult. However, with the rapid advancement of data science and machine learning techniques, data-driven health assessment methods are gaining attention among researchers. These methods make use of the large amount of real-time data generated by the component during operation. By analyzing and processing these data, key features are extracted. The health status of the component is assessed from a number of perspectives such as the probability of failure [5; 6; 7; 8; 9], remaining life [10; 11; 12; 13; 14], and degree of condition deviation [15; 16]. [PERSON] et al. [17] obtained operational data of flywheel motors through ground-based acceleration experiments, anomaly detection, and fault prediction of satellite flywheel motors using shifted nuclear particle filters. [PERSON] et al. [18] analyzed the cause of the failure of the EgyptSat 1 satellite through a machine learning approach. A multilayer bi-directional long short-term memory (Bi-LSTM)-based fault prediction algorithm was proposed by [PERSON] et al. [19] to improve the prediction accuracy of intelligent algorithms. In order to avoid excessive waste of resources, [PERSON] et al. proposed an optimized regression method based on the coyote optimization algorithm (COA) and support vector regression (SVR) to predict the remaining battery life [20]. [PERSON] et al. [21] presented an iterative update method to improve the long-term predictive performance of battery remaining life prediction. [PERSON] et al. [22] subjected both the input-to-state and state-to-state transitions of the LSTM to convolution operations, including the temporal frequency and temporal information of the signal. This resulted in better computational efficiency and prediction accuracy when predicting the remaining life of bearing components. The classical single-parameter correlation vector regression (RVR) was extended to a multiparameter model by [PERSON] et al. [23]. It was successfully applied to the remaining life prediction of capacitors and bearing components and good prediction results were achieved. However, due to the complexity of the orbital environment, the remaining lifetime of satellite components was strongly influenced by the random environment [24; 25] and there were difficulties in the validation process. In addition, the analysis of component failure probabilities required the support of a large number of samples, while the available failure samples were very limited [26; 27; 28]. [PERSON] et al. [15] proposed a real-time health assessment method for satellite components in orbit based on the characteristics of multiparametric data distributions. The method comprehensively considers the health of data distribution deviations (HDDDs) and the similarity of data changes (SOC).