Zhou Hang, Zhou Jinju, Li Yunchen, Cai Fanger
Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China.
Sci Rep. 2025 Mar 19;15(1):9511. doi: 10.1038/s41598-025-93343-4.
The widespread use of unmanned aerial vehicles (UAVs) stimulates the demand for condition monitoring methods. To this end, a wide range of condition monitoring methods have been developed to monitor UAVs' performance. However, since the relatively low accuracy of monitoring and high reliance on human experience for threshold setting, the performance of condition monitoring methods for UAVs is deficient. Therefore, this paper proposes an advanced condition monitoring method for UAVs which is composed of improved multivariate state estimation technique (IMSET) and a novel threshold setting method based on probability distribution. Firstly, the IMSET constructs memory matrix (MM) by dynamic selection with incremental learning to improve the accuracy of estimation. Secondly, the exponentially weighted moving average (EWMA) is employed to mitigate the impact of measurement errors in condition vectors and then the threshold is set by probability distribution to reduce the dependence on human experience. To verify the effectiveness of the proposed method, sufficient experiments based on condition monitoring data generated by DJI F450 are conducted. The experimental results demonstrate that the method proposed in this paper can accurately monitor the condition of UAVs in time.
无人机（UAV）的广泛使用刺激了对状态监测方法的需求。为此，人们开发了各种各样的状态监测方法来监测无人机的性能。然而，由于监测精度相对较低且阈值设置高度依赖人类经验，无人机状态监测方法的性能存在缺陷。因此，本文提出了一种先进的无人机状态监测方法，该方法由改进的多元状态估计技术（IMSET）和基于概率分布的新型阈值设置方法组成。首先，IMSET通过增量学习动态选择构建记忆矩阵（MM），以提高估计精度。其次，采用指数加权移动平均（EWMA）来减轻状态向量中测量误差的影响，然后通过概率分布设置阈值，以减少对人类经验的依赖。为验证所提方法的有效性，基于大疆F450生成的状态监测数据进行了充分的实验。实验结果表明，本文提出的方法能够及时准确地监测无人机的状态。
