A Method for Satellite Component Health Assessment Based on Multiparametric Data Distribution Characteristics

Abstract

This research presents a novel data-based multi-parameter health assessment method to meet the growing need for the in-orbit health assessment of satellite components. This method analyzed changes in component health status by calculating distribution deviations and variation similarities in real-time operational data. Firstly, a single-parameter health state description method based on data distribution characteristics was presented. Secondly, the main health characteristic parameters were selected by mechanistic analysis and expert experience. The CRITIC method and the entropy weighting method were fused to assign reasonable weights and establish a multi-parameter component health assessment model. Then, the feasibility of a component health assessment algorithm based on data distribution characteristics was verified using real telemetry data from satellites. Finally, to verify the rationality of the presented health assessment algorithm, the results were compared with the pre-processed original data using empirical mode decomposition. The experimental results show that the method can accurately describe the change trend of the health status of the components. It proves that the method can be effectively used for the real-time health condition assessment and monitoring of satellite components.

A Novel Approach to Satellite Component Health Assessment Based on the Wasserstein Distance and Spectral Clustering

“…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.…”

“…The temporary increase in the similarity of current data around day 2050 causes the SOC results to be temporarily unavailable, which needs to be considered together with the HDDD based on the MMD to ensure that no erroneous results occur. Therefore, the method proposed in reference [15] needs to consider these two indicators at the same time. These two indicators complement each other in performing a reasonable and complete component health assessment.…”

“…The results are shown in Table 1. The details of the method can be found in [15] and will not be repeated here. As can be seen in Figure 8, the current data are extremely stable for the first two years, with no significant changes in amplitude.…”

“…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]. Park 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.…”

“…Hui 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).…”