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.

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This article introduces the principle of aeroengine gas path electrostatic monitoring and establishes a mathematical model of aeroengine gas path debris electrostatic sensor. In this study, we simulate particle’s movement based on the established model and perform numerical analysis of the induced charge pulse waveform. The simulation results show the quantitative relationship among particle’s charge amount, velocity, and pulse waveform’s features, and obtain the qualitative relationship between particle’s spatial position and pulse waveform’s features. A test rig is designed to verify the correctness of the mathematical model. A measurement mode based on dual-channel sensors has been proposed, and corresponding signal processing methods are used to calculate the velocity of the particle and reconstruct the charge pulse waveform from the measured voltage signal. The conclusions of this study not only avoid the shortcomings of traditional signal processing methods that directly use the measured voltage signal but also have important significance for improving the electrostatic monitoring capability.

Purpose Electrostatic monitoring technology is a useful tool for monitoring and detecting component faults and degradation, which is necessary for engine health management. This paper aims to carry out online monitoring experiments of turbo-shaft engine to contribute to the practical application of electrostatic sensor in aero-engine. Design/methodology/approach Combined with the time and frequency domain methods of signal processing, the authors analyze the electrostatic signal from the short timescale and the long timescale. Findings The short timescale analysis verifies that electrostatic sensor is sensitive to the additional increased charged particles caused by abnormal conditions, which makes this technology to monitor typical failures in aero-engine gas path. The long scale analysis verifies the electrostatic sensor has the ability to monitor the degradation of the engine gas path performance, and water washing has a great impact on the electrostatic signal. The spectrum of the electrostatic signal contains not only the motion information of the charged particles but also the rotating speed information of the free turbine. Practical implications The findings in this article prove the effectiveness of electrostatic monitoring and contribute to the application of this technology to aero-engine. Originality/value The research in this paper would be the foundation to achieve the application of the technology in aero-engine.

In the processing of particulate solids, particle–particle and particle-wall collisions can generate electrostatics. This is called contact/impact/frictional electrification and can lead to many problems such as affecting powder flow and explosion hazards. It is necessary to research the tribo-electrification charging due to single particle impacts on a target as the fundamental understanding of tribo-electrification. A new impact charging test rig based on an electrostatic sensor array that can measure charge transfer caused by a single impact between a particle and a target plane has been designed and established. Combined with the electrostatic sensor array, the compressed sensing algorithm is used to estimate not only the spatial position but also the charge amount of particle. The cross-correlation algorithm is used to determine particle’s velocity instead of using other devices such as a photodetector. The new instrument allows single particles impacting target planes at different angles with a velocity exceeding 100 m/s. An oil calibration test rig has been constructed to verify the proposed methods. The estimation errors of the spatial position and charge amount are both within 5% when the particle is located at the central area of the pipeline and the estimation errors of velocities are within 2%. The impact charging experiments show a special initial charge prior to impact for which no net charge transfer would occur for polymer particles, but the charge would completely transfer for metal particles.