Federated fault diagnosis using data fusion in large-scale heterogeneous unmanned systems

Abstract

Heterogeneous unmanned systems (HUS) consist of multiple types of unmanned sub-systems, and when one or more sub-systems experience failures, it can severely impact the overall system’s operation. Therefore, establishing effective fault diagnosis(FD) methods is crucial for ensuring the safety and reliability of heterogeneous unmanned systems. This paper proposes a federated fault diagnosis method based on data fusion, which combines visual images and multi-sensor information to enhance the fault identification capability of heterogeneous unmanned systems in complex environments. By using an offline broad reinforcement learning strategy, we propose a Federated Broad Reinforcement Learning fault diagnosis method. It achieves high-precision fault diagnosis under various fault conditions by iteratively reconstructing fused data and knowledge. Finally, the proposed method is validated on a hardware-in-the-loop (HIL) simulator in large-scale heterogeneous unmanned systems. Experimental results show that the proposed method improves fault diagnosis accuracy and enhances the safety and reliability of the system.