Identification methods of charged particles based on aero-engine exhaust gas electrostatic sensor array

Abstract

This paper presents a study of aero-engine exhaust gas electrostatic sensor array to estimate the spatial position, charge amount and velocity of charged particle. Firstly, this study establishes a mathematical model to analyze the inducing characteristics and obtain the spatial sensitivity distribution of sensor array. Then, Tikhonov regularization and compressed sensing are used to estimate the spatial position and charge amount of particle based on the obtained sensitivity distribution; cross-correlation algorithm is used to determine particle’s velocity. An oil calibration test rig is established to verify the proposed methods. Thirteen spatial positions are selected as the test points. The estimation errors of spatial positions and charge amounts are both within 5% when the particles are locating at central area. The errors are higher when the particles are closer to the wall and may exceed 10%. The estimation errors of velocities by using cross-correlation are all within 2%. An air-gun test rig is further established to simulate the high velocity condition and distinguish different kinds of particles such as metal particles and non-metal particles.

Compressive sensing-based de-aliasing method for array electrostatic signal

“…We have proposed a variety of ESA configurations in our previous research (Guo et al , 2021b; Guo et al , 2022; Zhong et al , 2021a). This paper introduces a kind of ESA called ingested debris monitoring sensor array (IDMSA).…”

“…In our previous research (Guo et al , 2021b, 2021c; Guo et al , 2022; Zhong et al , 2021a; Zhong et al , 2021b), we proposed the aero-engine array electrostatic monitoring technology (AEMT), which is to apply the electrostatic sensor array (ESA) to the condition monitoring and fault diagnosis of aero-engine gas path components and lubricating components. The results of experiments and simulation analysis show that the ESA has good monitoring capability for charged particles in the gas path of aero-engine, and has the potential to improve the problems of high false alarm rate of traditional EMT.…”

An optimization method of electrostatic sensor array based on Kriging surrogate model and improved non-dominated sorting genetic algorithm with elite strategy algorithm

“…The stability of the measurement depends on the detection capability of a single sensing probe, so the mean value of the sensitivity of a single probe is used to characterize this metric, as shown in equation (8), where y i denotes the value of the ith element in any row vector of the S matrix with a dimension of N × m. In theory, better measurement stability can be obtained by maximizing F (l, e, N).…”

“…In our previous research, [8][9][10][11] we proposed the aeroengine array electrostatic monitoring technology which is to apply the electrostatic sensor array (ESA) to the condition monitoring and fault diagnosis of the aero-engine gas path components and lubrication components. Fifth generation aero-engines are equipped with electrostatic sensors to enhance the prediction and health management (PHM) capability, which means that the ability of electrostatic sensors to recognize particle information also needs to be improved.…”