Electrostatic cross-correlation sensitivity weighting based gas path debris monitoring
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摘要:
由于静电传感器阵列（ESA）安装位置及电极数目受到工作环境限制，导致航空发动机故障预测与健康管理（PHM）系统的线性独立测量信息数较少。针对此问题，提出了一种基于静电互相关（CC）灵敏度加权的气路碎片监测方法，在同一电极数目的条件下利用互相关聚焦原理有效增加了能够表征不同敏感区域的测量信息。在此基础上设计了8电极ESA，建立了不同电极对的互相关灵敏度分布，并以获取的16个相关速度值对其进行加权计算，计算结果反映了带电碎片的速度和位置信息。带式静电实验装置和颗粒落体实验装置的实验结果验证了所提方法的有效性，对单颗粒与多颗粒的监测结果和实际分布的平均相关系数分别达到了0.668与0.652，提高了PHM系统的监测信息量和稳定性。
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关键词:
- 互相关(CC)灵敏度 /
- 静电传感器阵列(ESA) /
- 故障预测与健康管理(PHM) /
- 气路碎片监测 /
- 航空发动机
Abstract: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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图 4 上下游各电极对单个带电颗粒的互相关速度
Figure 4. Cross-correlation speeds between upstream and downstream arbitrary pairs of electrodes to a single charged particle
图 5 经互相关速度加权后的任意对电极间互相关灵敏度分布
Figure 5. Cross-correlation velocity weighted cross-correlation sensitivity distributions between arbitrary pairs of electrodes
图 6 互相关灵敏度分布加权叠加后的速度分布
Figure 6. Velocity distribution after superposition of weighted cross-correlation sensitivity distributions
图 7 带式静电实验装置结构示意图及实物图
Figure 7. Structural diagram and photograph of belt electrostatic experimental device
图 8 验证实验ESA及橡胶带位置示意图
Figure 8. Location schematic diagram of ESA and rubber belt in validation experiment
图 9 不同电机转速所对应的速度分布测量结果
Figure 9. Velocity distribution measurement results corresponding to different motor rotation speeds
表 1 电机转速与橡胶带的换算速度
Table 1. Motor rotation speed and corresponding rubber belt velocity
电机转速/(r·min-1) 橡胶带速度/(m·s-1) 500 2.0944 750 3.1416 1000 4.1888 1250 5.2360 1500 6.2832 
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表 2 互相关灵敏度加权速度提取方法对不同空间分布的单颗粒及多颗粒速度测量结果
Table 2. Velocity measurement results of single particle and multiple particles with different spatial distributions by cross-correlation sensitivity weighting method
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