Distributed Fault Diagnosis for Heterogeneous Multi-Agent Systems: A Hybrid Knowledge-Based and Data-Driven Method

Heterogeneous Multi-Agents System (MAS) has been attracting increasing attention in many application areas, but the safety and reliability of MAS are still challenging issues. Fault diagnosis is a necessary technology to ensure the safety and reliability of heterogeneous MAS. According to the characteristics of high dispersion in MAS, strong local perception ability and weak global perception ability, this paper proposes a distributed hybrid knowledge-based and data-driven fault diagnosis, which realizes dynamic re-construction of data and knowledge through reinforcement learning and fuzzy broad learning. In the meantime, we also consider communication network topology to realize distributed collaborative diagnosis, which can effectively improve the diagnostic performance. Then, we develop a high-fidelity heterogeneous MAS software-in-the-loop and hardware-in-the-loop fault simulators to simulate different types of failures (i.e., actuator failure, sensor and communication failure). Finally, through the cross-validation on the above developed simulators, this work verifies the effectiveness of the proposed distributed intelligent fault diagnosis.