KELM based diagnostics for air vehicle faults

SONG Jia; SHI Ruoling; GUO Xiaohong; LIU Yang

doi:10.16511/j.cnki.qhdxxb.2020.26.005

PDF(4856 KB)

Journal of Tsinghua University(Science and Technology) ›› 2020, Vol. 60 ›› Issue (10) : 795-803. DOI: 10.16511/j.cnki.qhdxxb.2020.26.005

SPECIAL SECTION: FAULT TOLERANT COMPUTING

KELM based diagnostics for air vehicle faults

{{article.zuoZhe_EN}}

Author information +

History +

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 words

fault diagnosis / hypersonic vehicle / reaction control system / kernel extreme learning machine

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

SONG Jia, SHI Ruoling, GUO Xiaohong, LIU Yang. KELM based diagnostics for air vehicle faults[J]. Journal of Tsinghua University(Science and Technology). 2020, 60(10): 795-803 https://doi.org/10.16511/j.cnki.qhdxxb.2020.26.005

References

[1] LI G J. Current research and new trends on flight control of air-breathing hypersonic cruise vehicles[C]//The 26th Chinese Control and Decision Conference. Changsha:IEEE, 2014:956-961.
[2] 董春玲, 赵越, 张勤. 动态故障诊断中的立体因果建模与不确定性推理方法[J]. 清华大学学报(自然科学版), 2018, 58(7):614-622.DONG C L, ZHAO Y, ZHANG Q. Cubic causality modeling and uncertain inference method for dynamic fault diagnosis[J]. Journal of Tsinghua University (Science and Technology), 2018, 58(7):614-622. (in Chinese)
[3] HUANG G B, ZHU Q Y, SIEW C K. Extreme learning machine:A new learning scheme of feedforward neural networks[C]//2004 IEEE International Joint Conference on Neural Networks. Budapest, Hungary:IEEE, 2004:985-990.
[4] KONGSOROT Y, HORATA P. Multi-label classification with extreme learning machine[C]//2014 6th International Conference on Knowledge and Smart Technology. Chonburi, Thailand:IEEE, 2014:81-86.
[5] 土松江·卡日, 高文胜, 张紫薇, 等. 基于支持向量机和遗传算法的变压器故障诊断[J]. 清华大学学报(自然科学版), 2018, 58(7):623-629.KARI T, GAO W S, ZHANG Z W, et al. Power transformer fault diagnosis based on a support vector machine and a genetic algorithm[J]. Journal of Tsinghua University (Science and Technology), 2018, 58(7):623-629. (in Chinese)
[6] 齐乃明, 周啟航, 秦昌茂. 高超声速飞行器六自由度建模及耦合特性分析[J]. 弹箭与制导学报, 2012, 32(1):49-52. QI N M, ZHOU Q H, QIN C M. The six DOF model of hypersonic vehicle and coupling characterization analysis[J]. Journal of Projectiles, Rockets, Missiles & Guidance, 2012, 32(1):49-52. (in Chinese)
[7] 李晓宇. 高超声速飞行器一体化布局气动外形设计[D]. 长沙:国防科学技术大学, 2007. LI X Y. Airframe design of hypersonic integrated vehicle[D]. Changsha:National University of Defense Technology, 2007. (in Chinese)
[8] 许域菲. 近空间飞行器非线性容错控制技术研究[D]. 南京:南京航空航天大学, 2011. XU Y F. Nonlinear fault tolerant control for near space vehicle[D]. Nanjing:Nanjing University of Aeronautics and Astronautics, 2011. (in Chinese)
[9] 赵汉元. 飞行器再入动力学和制导[M]. 长沙:国防科技大学出版社, 1997. ZHAO H Y. Aero dynamic and guidance of reentry vehicle[M]. Changsha:School of Defense Science and Technology Press, 1997. (in Chinese)
[10] 秦昌茂. 高超声速飞行器分数阶PID及自抗扰控制研究[D]. 哈尔滨:哈尔滨工业大学, 2011. QIN C M. Research on fractional order PID controller and ADRC for hypersonic vehicle[D]. Harbin:Harbin Institute of Technology, 2011. (in Chinese)
[11] LOU X S, LOPARO K A. Bearing fault diagnosis based on wavelet transform and fuzzy inference[J]. Mechanical Systems & Signal Processing, 2004, 18(5):1077-1095.
[12] LIU Z H, SU J, QIN C, et al. Wavelet packet decomposition and grey relational analysis application in fault diagnosis of aero hydraulic pump[C]//IEEE International Conference on Aircraft Utility Systems. Beijing:IEEE, 2016:734-738.
[13] SIMON D. Biogeography-based optimization[J]. IEEE Transactions on Evolutionary Computation, 2008, 12(6):702-713.
[14] HSU C W, CHANG C C, LIN C J. A practical guide to support vector classification[J]. BJU International, 2008, 101(1):1396-1400.
[15] 卢明, 刘黎辉, 吴亮红. 多核支持向量数据描述分类方法研究[J]. 计算机工程与应用, 2016, 52(18):68-73. LU M, LIU L H, WU L H. Research on multi-kernel support vector data description method of classification[J]. Computer Engineering and Applications, 2016, 52(18):68-73. (in Chinese)
[16] 李素, 李鲁文, 庄大方, 等. 混合核函数研究及其在数据建模领域应用进展[J]. 计算机仿真, 2015, 32(7):1-6. LI S, LI L W, ZHUANG D F, et al. Research on mixed kernel function and its application in the field of data modeling[J]. Computer Simulation, 2015, 32(7):1-6. (in Chinese)
[17] 周志官, 郭韵, 李渴望. 改进核函数的支持向量机智能诊断方法研究[J]. 轻工机械, 2016, 34(5):23-26.ZHOU Z G, GUO Y, LI K W. Intelligent diagnosis method of improved kernel function SVM[J]. Light Industry Machinery, 2016, 34(5):23-26. (in Chinese)