Auxiliary power unit is one of the indispensable systems for civil aviation aircraft but the traditional planned maintenance cannot meet the actual needs of airlines. In this work, the key performance parameters of the auxiliary power unit are selected by using recursive feature elimination method. With the selected parameters, the remaining useful flight cycle of the auxiliary power unit is predicted by applying particle filter techniques. Some improved algorithms such as Gaussian particle filter and auxiliary particle filter are also compared. The experimental results demonstrate that the particle filter-based method has high prediction accuracy and engineering application value.

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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.

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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