基于多步神经网络观测器的扑翼飞行器缓变故障检测

针对缓变故障初始变化幅值较小导致的基于传统神经网络观测器的故障检测算法检测效率较低的问题，提出一种基于多步神经网络观测器与自适应阈值的扑翼飞行器（FWMAV）缓变故障检测算法。首先，构建一个多步预测的观测器模型，利用多步观测器的延时性能避免观测器被故障数据污染；然后，依据FWMAV的实际飞行实验数据，对多步观测器窗口宽度进行实验和分析；其次，提出一种自适应阈值策略，通过残差卡方检测算法辅助进行观测器残差值的故障检测；最后，采用FWMAV的实际飞行实验数据进行算法的验证和分析。结果表明，与基于传统神经网络观测器的故障检测算法相比，所提算法在缓变故障检测速度方面提升了737.5%，在缓变故障检测准确率方面提升了96.1%。由此可见，所提算法能够有效提高FWMAV缓变故障的检测速度和检测准确率。

Soft fault detection for flapping wing micro aerial vehicle based on multistep neural network observer

Since the small initial variation amplitude of soft fault leads to the low detection efficiency of fault detection algorithm based on traditional neural network observer, a soft fault detection algorithm for Flapping Wing Micro Aerial Vehicle (FWMAV) based on multistep neural network observer and adaptive threshold was proposed. Firstly, a multistep prediction observer model was constructed, and the time-delay ability of it can prevent the observer from being polluted by faulty data. Secondly, the window width of the multistep observer was tested and analyzed according to the actual flight data of FWMAV. Thirdly, an adaptive threshold strategy was proposed to perform the fault detection of the observer residuals with the assistance of residual chi-square detection algorithm. Finally, the proposed algorithm was verified and analyzed with the use of actual flight data of FWMAV. Experimental results show that compared with the fault detection algorithm based on traditional neural network observer, the proposed algorithm has the soft fault detection speed increased by 737.5%, and the soft fault detection accuracy increased by 96.1%. It can be seen that the proposed algorithm can effectively improve the soft fault detection speed and accuracy of FWMAV.