# Mission-Oriented Real-Time Health Assessments of Microsatellite Swarm Orbits
###### Abstract  
Real-time health assessments are of great importance for the safe and stable operation of in-orbit swarms. To solve the problems of existing real-time health assessments of microsatellite swarms, such as the difficulty of selecting a multisource and assessment calculation normalization, this paper proposes a real-time health assessment method applicable to mission-oriented swarms. The method divides the microsatellite swarm into three levels: single satellite, intersatellite communication link and swarm effectiveness, which establish a multilevel index system by adopting the reliability evaluation based on random failure and failure by loss, a health evaluation based on natural connectivity, and a real-time dynamic analysis based on swarm topology. For the swarm effectiveness during the mission, the multilevel index and the entropy weight method are used to construct the effectiveness evaluation model of the whole swarm, and the health state evaluation of the swarm is realized based on the variable weight principle. The simulation results show that this method can quantify the health state of the microsatellite swarm in real-time, and it can predict the health state after the fault without maintenance. microsatellite; real-time health assessment; swarm health; variable-weight synthesis principle; topology +
Footnote †: journal: Applied  
Article  
Mission-oriented microsatellite clustering technology has become one of the future trends in space technology development due to its advantages in mission executability, system robustness, and polarizability [1; 2; 3]. However, with the dramatic increase in the size of the satellites in orbit, the stability, safety, and reliability of the on-orbit operation of the swarm are challenged [4], and the need for health management techniques for satellite swarms is prominent. In general, a satellite swarm consists of multiple satellites in concert, and the failure of a few satellites cannot be considered the failure of the whole swarm. Since assessing the swarm status is a prerequisite for in-orbit management, health assessment is of great significance for the swarm health management [5]. The in-orbit real-time assessment of satellite swarm health status has been a difficult task in the field of health management. The current research objects of in-orbit health assessment are mainly spacecraft key components and control systems, which are usually studied based on mathematical models or data. For the satellite attitude control system (ACS), [PERSON] et al. [6] improved the fuzzy synthetic evaluation (FSE) method for a quantitative assessment of the satellite attitude control system health, including dimensionless processing of specified characteristic parameters (CPs), rule-based evaluation, and common fuzzy synthetic assessment. [PERSON] et al. [7] proposed a real-time in-orbit health assessment method for satellite attitude control systems based on multilevel fuzzy comprehensive evaluation architecture, which can realize the health assessment of attitude control systems. [PERSON] et al. [8] introduced the concept of degraded state or partial failure, thoroughlyanalyzed the effects of different failures of the system, and constructed a satellite attitude control system Petri network model, and the remaining lifetime can be predicted by a large number of simulations. In the evaluation studies of swarms, the study of the effectiveness assessment dominates. However, the aforementioned studies rely on experts and large amounts of data, which lack objectivity and practicality. [PERSON] et al. [9] proposed a health assessment algorithm combining support vector machine (SVM) and cloud model to achieve spacecraft power control system health state measurement as a support for online health assessment and operational decision making of power critical components. [PERSON] et al. [10] introduced a convolutional sparse model for anomaly detection in mixed continuous and discrete data, based on evaluation on a representative dataset composed of real spacecraft telemetry.