A Health Diagnosis Method for Aeroengine Gas Circuit Components Based on Particle Filter

Abstract

The invention discloses an aero-engine gas path component health diagnosis method based on particle filtering. The aero-engine gas path component health diagnosis method includes the steps that a nonlinear mathematical model of an engine is established; a particle filtering algorithm is designed based on significance weight value adjustment of a neural network; finally, a gas path component health diagnosis is achieved based on the nonlinear model of the engine by the adoption of the designed algorithm. The nonlinear mode is that on the basis of a physical equation reflecting the aerothermodynamics performance of the engine, a shared working equation set among the components is established, and by the adoption of a Newton Laphson interactive algorithm, the nonlinear equation set is solved to obtain working parameters of the cross section of the engine; the particle filtering algorithm based on the significance weight value adjustment of the neural network is that a BP neural network algorithm and a typical sampling algorithm are combined, on the basis of a standard particle filtering algorithm, two steps of weight value splitting and particle adjustment are added, and therefore the phenomena of particle degradation and sample depletion are effectively avoided. The health diagnosis of gradual performance degradation and sudden faults of the gas path components within the service life of the engine can be achieved.

Description

Technical field: [0001] The invention relates to a method for diagnosing the health of air circuit components of an aero-engine based on particle filtering, which belongs to the field of aero-engine fault diagnosis. Background technique: [0002] The structure of aero-engine is becoming more and more complex, and the working conditions are harsh and changeable, which belongs to the fault-prone system. In the maintenance of the US Air Force, the cost of routine maintenance and replacement is very huge, of which the cost of the engine accounts for 60%. Therefore, in order to make the engine run safely and efficiently and save maintenance costs, it is necessary to understand the operating conditions of the engine, master its performance change rules, and conduct health diagnosis on key components. Studies have shown that in the overall failure of aeroengines, the failure of gas path components accounts for more than 90%. Therefore, the health diagnosis of gas path components p...

AI Technical Summary

Problems solved by technology

However, EKF and UKF are subject to the constraints of the Kalman filter framework, and there are also certain limitations in the application of non-Gaussian systems. Particle filter (PF) is a new technology based on Monte Carlo method and recursive Bayeux that has emerged in recent years. The statistical filtering method of the Adams estimation replaces the integral operation with the sample mean value, and uses the discrete random measure composed of particles and their weights to approximate the posterior probability distribution of the state. It is suitable for systems with strong nonlinearity and has no restrictions on the noise distribution characteristics, but As an emerging algorithm, PF is not yet mature enough. There are mainly problems such as particle degradation, sample depletion, large amount of calculation, and proof of convergence. These are the focus of current research.

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