Rapid fault diagnosis method for turbofan engine based on improved Gaussian particle filter

Abstract

The invention discloses a turbofan engine mutation fault diagnosis method based on the improved Gaussian particle filter, relates to the field of aeroengine fault diagnosis, can realize rapid diagnosis of mutation faults, has low noise level of diagnosis results, and high accuracy of estimation results. The invention includes: injecting the engine nonlinear mathematical model into the sudden failure of gas circuit components; designing an improved Gaussian particle filter algorithm based on pseudo-covariance; loading the engine nonlinear mathematical model into the improved Gaussian particle filter algorithm, and improving the gas circuit Diagnose component mutation faults and obtain diagnosis results. The invention adopts pseudo-covariance instead of covariance and Gaussian sampling instead of re-sampling, which reduces the diagnosis time, improves the diagnosis accuracy, and can realize the rapid diagnosis of gas path mutation faults within the life cycle of the engine.

Description

technical field [0001] The invention relates to the field of fault diagnosis of aeroengines, in particular to a method for sudden fault diagnosis of a turbofan engine based on improved Gaussian particle filtering. Background technique [0002] The turbofan engine has complex structure and harsh working environment, which is a kind of failure-prone system. According to statistics, the failure of turbofan engine gas path components accounts for more than 90% of the total turbofan engine failures. Therefore, real-time detection of engine health status and analysis of gas path performance are important ways to improve engine safety and reliability. The health parameters such as the efficiency variation coefficient and the flow variation coefficient of the engine gas path components are the state characteristics of the engine gas path fault, and these health parameters will directly lead to changes in engine measurement parameters such as speed, temperature, and pressure. Therefo...

AI Technical Summary

Problems solved by technology

There are two main problems in the standard particle filter used in engine fault diagnosis: (1) Since the standard particle filter directly uses the prior distribution as the importance density function, and does not combine the latest observations, the sudden change cannot be realized under the mutation fault Rapid diagnosis of faults; (2) Due to the high dimension of the engine health parameters, the sampling rate of the filter is low, and at the same time, due to real-time considerations, the number of particles is small, so the number of effective particles is low, resulting in high noise levels in diagnostic results and low estimation accuracy

[0004] Therefore, in the prior art, there is a lack of a particle filter algorithm, which can realize rapid diagnosis of mutational faults, and the noise level of the diagnosis result is low, and the estimation result is highly accurate.

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