| Citation: | LI R Z，JIANG B，YU Z Q，et al. Data-driven fault detection and diagnosis for UAV swarms[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（5）：1586-1592 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0441
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The security requirements of the unmanned aerial vehicle (UAV) swarm system are extremely strict, and real-time fault detection and diagnosis is one of its important supporting technologies. This paper presents a fault diagnosis method based on statistical model and improved broad learning system (BLS) model. Firstly, the behavior characteristics of UAV swarm system under normal and different fault modes are characterized by multivariate data statistical analysis, and then the improved BLS model is used to achieve accurate and rapid fault diagnosis. On this basis, a high fidelity simulation verification platform is developed to verify the rationality and effectiveness of the proposed method. The experimental results show that the proposed method has obvious diagnostic advantages compared with the current mainstream methods.
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