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清华大学学报(自然科学版)  2020, Vol. 60 Issue (10): 795-803    DOI: 10.16511/j.cnki.qhdxxb.2020.26.005
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基于核极限学习机的飞行器故障诊断方法
宋佳1, 石若凌1, 郭小红2, 刘杨3
1. 北京航空航天大学 宇航学院, 北京 100191;
2. 西安卫星测控中心, 西安 710043;
3. 北京航空航天大学 自动化科学与电气工程学院, 北京 100191
KELM based diagnostics for air vehicle faults
SONG Jia1, SHI Ruoling1, GUO Xiaohong2, LIU Yang3
1. School of Astronautics, Beihang University, Beijing 100191, China;
2. Xi'an Satellites Measure&Control Center of China, Xi'an 710043, China;
3. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
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摘要 针对高超声速飞行器反作用控制系统(reaction control system,RCS)的推力器故障,展开了基于核极限学习机(kernel extreme learning machine,KELM)的故障诊断方法研究,并对该诊断方法进行了参数优化和核函数优化,为飞行器执行器故障提供了快速准确的诊断方法。结果表明:该方法可以克服对飞行器模型的依赖,以数据驱动的方式对飞行器执行器故障实现快速准确的诊断。
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宋佳
石若凌
郭小红
刘杨
关键词 故障诊断高超声速飞行器反作用控制系统核极限学习机    
Abstract:A fault diagnosis method based on a kernel extreme learning machine (KELM) was developed to analyze thruster failures in hypersonic aircraft reaction control systems (RCS). The parameters and kernel function were optimized for faults involving aircraft actuator failures. Results using this fast, accurate diagnostic method show that the method is not dependent on the aircraft model and provides fast and accurate diagnoses of aircraft actuator faults using a data-driven process.
Key wordsfault diagnosis    hypersonic vehicle    reaction control system    kernel extreme learning machine
收稿日期: 2019-09-16      出版日期: 2020-07-09
引用本文:   
宋佳, 石若凌, 郭小红, 刘杨. 基于核极限学习机的飞行器故障诊断方法[J]. 清华大学学报(自然科学版), 2020, 60(10): 795-803.
SONG Jia, SHI Ruoling, GUO Xiaohong, LIU Yang. KELM based diagnostics for air vehicle faults. Journal of Tsinghua University(Science and Technology), 2020, 60(10): 795-803.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2020.26.005  或          http://jst.tsinghuajournals.com/CN/Y2020/V60/I10/795
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
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