A Spacecraft Fault Diagnosis Method Based on Graph Attention Network and DDPG Algorithm

In this paper, we improve the deep deterministic policy gradient algorithm and combine the graph attention network to propose a spacecraft fault diagnosis method. Based on the construction of spacecraft system level and component level knowledge graphs, a unique reward function, policy network and value network are set up according to the structure of spacecraft knowledge graphs and the semantic configuration of reinforcement learning environment. Based on the construction of spacecraft system level and component level knowledge graphs, unique reward functions, environments, policy networks and value networks are set according to the structure and semantics of spacecraft knowledge graphs. We use in orbit data for experimental validation, and the experimental results show that the method can combine systemlevel knowledge graph with component level knowledge graph for hierarchical, fast and accurate fault diagnosis.