## 2 Problem Description and Overall Program  
### Problem Description  
This research aims to optimize the original method to address two specific challenges, with the goal of providing a more versatile approach to component health assessment.  
First, the MMD mentioned in [15] is capable of describing departures in the data distribution. However, it solely focuses on overall differences and fails to consider variations in the location distributions. Therefore, it needs to be complemented with Pearson coefficients to capture the similarities of data changes (SOC). The challenge lies in effectively combining both indicators when making decisions regarding assessment results. Additionally, different mapping functions are chosen for different types of data variations. This restricts the original method to being suitable only for the multiparameter fusion evaluation of the same type of parameters within a component. For instance, the original method can only select current data exhibiting the same type of variation across the four axes of a gyro. Another example is temperature data demonstrating the same type of variation on a solar sail. Figure 1a illustrates the current data for the four axes of a gyro, indicating that they vary in precisely the same manner. In contrast, Figure 1b depicts the bearing temperature and current data for a momentum wheel, revealing that they do not exhibit the same type of variation.  
Figure 2 shows the results of the MMD calculations for two sets of data with different types of changes. The second set of data in each graph is obtained by adding one to the first set of data magnitudes, with no other changes. However, the MMD results obtained at this point in the calculation are different. In short, the calculated MMD results are not the same  
Figure 1: Variation in health characteristic parameters of different components.  
when different variation types of parameters have the same degree of data distribution deviation (DDD).  
Considering the need to combine different types of parameters for a more comprehensive component health assessment, another consideration is to simplify the decision-making process for the assessment results. This research introduces the WD as a proxy indicator to describe the differences in the data distribution and to provide more comprehensive, accurate, and interpretable assessment results [29; 30].  
Second, the determination of health status thresholds for satellite components traditionally relies on expert experience. However, expert experience is highly subjective, difficult to update, and may not fully leverage the potential information present in the data. To overcome these limitations, this research introduces a spectral clustering approach to determine threshold values. Spectral clustering is a nonparametric clustering method that does not require assumptions to be made about the data distribution. It is applicable to various types of data, including health status monitoring data of satellite components. Spectral clustering has the advantages of adapting to complex data structures, taking into account global information, scalability, and the absence of prelabeled samples [31]. It enables a more scientific, accurate, and reasonable classification of component health status.