Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (8): 2449-2454.DOI: 10.11772/j.issn.1001-9081.2020010107

• Frontier & interdisciplinary applications • Previous Articles     Next Articles

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

WANG Sipeng, DU Changping, YE Zhixian, SONG Guanghua, ZHENG Yao   

  1. School of Aeronautics and Astronautics, Zhejiang University, Hangzhou Zhejiang 310027, China
  • Received:2020-02-07 Revised:2020-03-11 Online:2020-08-10 Published:2020-03-11
  • Supported by:
    This work is partially supported by the Key Project of Joint Fund of Ministry of Education for Equipment Pre-research (6141A02011803).

基于多步神经网络观测器的扑翼飞行器缓变故障检测

王思鹏, 杜昌平, 叶志贤, 宋广华, 郑耀   

  1. 浙江大学 航空航天学院, 杭州 310027
  • 通讯作者: 杜昌平(1978-),男,安徽和县人,副教授,博士,主要研究方向:导航制导与控制、复杂系统建模与仿真、多传感器数据融合,duchangping@zju.edu.cn
  • 作者简介:王思鹏(1997-),男,辽宁大连人,硕士研究生,主要研究方向:导航制导与控制、多传感器数据融合;叶志贤(1993-),男,江西南昌人,博士研究生,主要研究方向:实验流体力学;宋广华(1968-),男,浙江海宁人,教授,博士,主要研究方向:卫星通信、空间数据系统;郑耀(1963-),男,浙江玉环人,教授,博士,主要研究方向:飞行器设计、航空宇航推进理论与工程。
  • 基金资助:
    装备预研教育部联合基金(重点)项目(6141A02011803)。

Abstract: 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.

Key words: soft fault, neural network, multistep observer, Flapping Wing Micro Aerial Vehicle (FWMAV), adaptive threshold

摘要: 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。

关键词: 缓变故障, 神经网络, 多步观测器, 扑翼飞行器, 自适应阈值

CLC Number: