Volume 46 Issue 3
Mar.  2020
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WANG Chao, ZHANG Shuai, LI Yadong, et al. Electrostatic cross-correlation sensitivity weighting based gas path debris monitoring[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(3): 457-464. doi: 10.13700/j.bh.1001-5965.2019.0102(in Chinese)
Citation: WANG Chao, ZHANG Shuai, LI Yadong, et al. Electrostatic cross-correlation sensitivity weighting based gas path debris monitoring[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(3): 457-464. doi: 10.13700/j.bh.1001-5965.2019.0102(in Chinese)

Electrostatic cross-correlation sensitivity weighting based gas path debris monitoring

doi: 10.13700/j.bh.1001-5965.2019.0102
Funds:

National Natural Science Foundation of China 61673291

More Information
  • Corresponding author: WANG Chao, E-mail: wangchao@tju.edu.cn
  • Received Date: 13 Mar 2019
  • Accepted Date: 18 Oct 2019
  • Publish Date: 20 Mar 2020
  • Due to the limit of the installation location and the electrode number of the electrostatic sensor array (ESA), the linear independent measurement information of the aeroengine fault prognostics and health management (PHM) system is rare. Aimed at this problem, this paper proposes an electrostatic cross-correlation (CC) sensitivity weighting based exhaust debris monitoring method. With the same electrode number, the CC focus method is applied to effectively enhance the number of measurement information that can characterize different sensitive regions. On this basis, the 8-electrode ESA is designed, and the CC sensitivity distribution of different electrode pairs is established and weighted by 16 correlation velocities. The results can reflect the velocity and location information of charged debris. The effectiveness of the method is validated by experiment results of the belt-style electrostatic induction experimental facility and the vertical gravity experimental device. The average correlation coefficients between the monitoring results of single particles and multiple particles and the actual distribution reached 0.668 and 0.652, respectively, which enhanced monitor information and stability of PHM system.

     

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