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山东大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (5): 215-222.doi: 10.6040/j.issn.1672-3961.0.2017.274

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动态核主元分析在无人机故障诊断中的应用

李明虎1,2,李钢2,钟麦英1*   

  1. 1.山东科技大学电气与自动化工程学院, 山东 青岛 266590;2.北京航空航天大学仪器科学与光电工程学院, 北京 100191
  • 收稿日期:2017-05-27 出版日期:2017-10-20 发布日期:2017-05-27
  • 通讯作者: 钟麦英(1965— ), 女,山东淄博人,教授,博士生导师,主要研究方向为故障诊断. E-mail: myzhong@buaa.edu.cn E-mail:liminghu@buaa.edu.cn
  • 作者简介:李明虎(1992— ),男,河北衡水人,硕士研究生,主要研究方向为故障诊断. E-mail: liminghu@buaa.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(61333005,61421063);山东省泰山学者优势特色学科人才团队资助项目

Application of dynamic kernel principal component analysis in unmanned aerial vehicle fault diagnosis

LI Minghu1,2, LI Gang2, ZHONG Maiying1*   

  1. 1. School of electrical and Automation Engineering, Shandong University of Science and Technology, Qingdao 266590, Shandong, China;
    2. School of Instrument Science and Opto-electronics Engineering, Beihang University, Beijing 100191, China
  • Received:2017-05-27 Online:2017-10-20 Published:2017-05-27

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摘要： 飞行控制系统作为无人机(unmanned aerial vehicle, UAV)的核心子系统,对其进行故障诊断可以大大提高无人机的安全性和可靠性。在无人机数学模型未知或者不确定的情况下,数据驱动的故障诊断方法比基于模型的方法更实用。考虑无人机飞行控制系统是典型的非线性动态系统,采用一种非线性主元分析方法对其进行故障诊断。利用数据建立无人机飞行控制系统正常状态下的动态核主元模型,通过T2和SPE两种统计量实现故障检测;故障发生后,利用重构贡献图的方法进行故障分离。仿真试验证明,该方法能对典型的无人机执行器和传感器故障进行有效监测和诊断。与动态主元分析相比,动态核主元分析方法对微小故障更为敏感。

关键词: 无人机, 飞行控制系统, 动态核主元分析, 重构贡献图, 故障诊断

Abstract: The flight control system(FCS)was the core subsystem of unmanned aerial vehicle(UAV), performing FD for it could greatly improve the safety and reliability of UAV. When the mathematical model of UAV was unknown or uncertain, data-driven methods were more suitable than model-based FD methods. Considering that FCS of UAV was a typical nonlinear dynamic system, a nonlinear principal component analysis(PCA)method was used instead. A dynamic kernel principal component model under normal state was established for UAV, then fault detection was performed by T2 and SPE statistics; When a fault was detected, a method called reconstruction-based contribution was used for fault isolation. The simulation results showed that the proposed method could achieve better fault diagnosis effect for typical faults of actuators and sensors than linear DPCA model. Besides, DKPCA could achieve high sensitivity for small faults of UAV.

Key words: fault diagnosis, DKPCA, UAV, flight control system, reconstruction-based contribution

中图分类号: 

  • TP206
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