基于数据驱动的无人机集群故障检测与诊断

Data-driven fault detection and diagnosis for UAV swarms

无人机集群系统对安全性和稳定性要求极高,实时的故障检测与诊断技术是保证安全可靠运行的有力手段。提出一种基于统计模型和改进宽度学习(BLS)模型的故障诊断方法。所提方法通过多元数据统计分析表征无人机集群系统在正常与不同故障模式下的行为特征,采用改进的BLS模型实现准确、快速的故障诊断。在此基础上,开发无人机集群系统的高逼真可视化仿真验证平台对所提方法的合理性与有效性进行验证。实验结果表明,所提方法与目前主流方法相比具有明显的诊断优势。

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.