A Fusion Diagnosis Method for Aeroengine Gas Path Faults

Abstract

The invention discloses an aircraft engine gas path fault fusion diagnosis method. The aircraft engine gas path fault fusion diagnosis method includes the steps that an engine state variable model is built firstly, sensor bias values (NL, NH, P3 and T6) obtained when an engine with a gas path fault is in a quasi-stable state in a flight envelope, then an optimal particle is obtained through a Kalman filter according to an electromagnetism-like mechanism method, and the sensor bias values (NL, NH, P3 and T6) of the engine with the gas path fault and gas path component health parameters are obtained finally. The aircraft engine gas path fault fusion diagnosis method breaks through limitation that a traditional gas path fault diagnosis method can only achieve single fault mode diagnosis, and can achieve health diagnosis of three broad categories of fault modes including the engine sensor fault, the gas path component fault and the sensor and gas path component simultaneous fault.

Description

technical field [0001] The invention relates to the field of aero-engine fault diagnosis, in particular to a fusion diagnosis method for an aero-engine sensor fault and an air path fault. Background technique [0002] Aeroengines work in the harsh environment of high temperature, high speed, strong vibration, and high stress. The working state of the engine changes frequently, and it bears variable loads, which requires extremely high safety and reliability. Due to the limitations of engine manufacturing, technology level and materials, as well as the level of later use, maintenance and management, the cost of design, manufacture and use and repair is high, so the health management and condition-based maintenance of the engine play a vital role in the safety, reliability and economic performance of the engine. important role. Engine health management refers to the monitoring of the working status of the engine, corresponding fault diagnosis and isolation, and maintenance an...

AI Technical Summary

Problems solved by technology

Factors such as high temperature and high pressure accompanied by strong vibration often cause the sensor measurement value to drift, which makes it difficult for the sensor measurement results to reflect the real state of the engine, and it is therefore difficult for the Kalman filter to obtain accurate estimates of the performance parameters of the engine components

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