中国邮电高校学报(英文版) ›› 2021, Vol. 28 ›› Issue (4): 53-63.doi: 10.19682/j.cnki.1005-8885.2021.2005

• Signal processing • 上一篇    下一篇

ESO-KELM-based minor sensor fault identification

Zhao Kai, Song Jia, Wang Xinlong   

  1. School of Astronautics, Beihang University, Beijing 100191, China
  • 收稿日期:2020-08-27 修回日期:2021-06-08 接受日期:2021-07-29 出版日期:2021-08-31 发布日期:2021-10-11
  • 通讯作者: Corresponding author: Zhao Kai, E-mail: 1961719909@qq.com E-mail:1961719909@qq.com
  • 基金资助:
    This work was supported by the National Natural Science Foundation of China (62073020).

ESO-KELM-based minor sensor fault identification

Zhao Kai, Song Jia, Wang Xinlong   

  1. School of Astronautics, Beihang University, Beijing 100191, China
  • Received:2020-08-27 Revised:2021-06-08 Accepted:2021-07-29 Online:2021-08-31 Published:2021-10-11
  • Contact: Corresponding author: Zhao Kai, E-mail: 1961719909@qq.com E-mail:1961719909@qq.com
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (62073020).

摘要: Aiming at the sensor faults of near-space hypersonic vehicles (NSHV), a fault identification method based on the extended state observer and kernel extreme learning machine (ESO-KELM) is proposed in this paper. The method is generated by a combination of the model-based method and the data-driven method. As the source of the fault diagnosis, the residual signals represent the difference between the ESO output and the result measured by the sensor in particular. The energy of the residual signals is distributed in both low frequency bands and high frequency bands. However, the energy of the sensor concentrates on the low-frequency bands. Combined with more different features detected by KELM, the proposed method devotes to improving the accuracy. Meanwhile, it is competent to calculate the magnitude of minor faults based on time-frequency analysis. Finally, the simulation is performed on the longitudinal channel of the Winged-Cone model published by the national aeronautics and space administration (NASA). Results show the validity and the accuracy in calculating the magnitude of the minor faults.

关键词: minor fault diagnosis, near-space hypersonic vehicles, extended state observer, kernel extreme learning machine, wavelet packet decomposition

Abstract: Aiming at the sensor faults of near-space hypersonic vehicles (NSHV), a fault identification method based on the extended state observer and kernel extreme learning machine (ESO-KELM) is proposed in this paper. The method is generated by a combination of the model-based method and the data-driven method. As the source of the fault diagnosis, the residual signals represent the difference between the ESO output and the result measured by the sensor in particular. The energy of the residual signals is distributed in both low frequency bands and high frequency bands. However, the energy of the sensor concentrates on the low-frequency bands. Combined with more different features detected by KELM, the proposed method devotes to improving the accuracy. Meanwhile, it is competent to calculate the magnitude of minor faults based on time-frequency analysis. Finally, the simulation is performed on the longitudinal channel of the Winged-Cone model published by the national aeronautics and space administration (NASA). Results show the validity and the accuracy in calculating the magnitude of the minor faults.

Key words: minor fault diagnosis, near-space hypersonic vehicles, extended state observer, kernel extreme learning machine, wavelet packet decomposition

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