Bayesian Semantic-Guided Attribute Transfer-Based Dual-Driven Fault Diagnosis for UAVs Swarm Systems With Unseen Faults

This article proposes a new hierarchical Bayesian semantic-guided attribute transfer (HBSAT)-based data-physics dual-driven fault diagnosis (FD) method for uncrewed aerial vehicle (UAV) swarm systems with unseen faults. First, based on the designed hierarchical fault attributes of UAV swarm systems, the HBSAT is developed to progressively learn highly matched correspondences between fault features and attribute semantics from available fault samples for learning attribute knowledge and attribute-related feature representations, which can be transferred to diagnose unseen faults. Furthermore, a mathematical model of the UAV swarm system is established to generate simulated unseen fault data consistent with fault attributes, which can help the FD model to learn more features and attributes related to unseen faults and improve the diagnostic performance. Besides, the proposed network is extended into the Bayesian deep learning framework to quantify uncertainty. The validity and advantages of the proposed approach are verified based on a semiphysical platform of a fixed-wing UAV swarm system.