Integrated diagnosis method of gas path fault of aeroengine

Abstract

The invention relates to an integrated diagnosis method of a gas path fault of an aeroengine, wherein the method is based on an adaptive weighted D-S evidence theory. With the method, problems that the precision is low because of a single fault diagnosis method and the evidence conflict exists during the fusion process of multiple methods can be solved. A non-linear local diagnosis module based on a particle filter and a non-linear local diagnosis module based on an extreme learning machine are constructed respectively; and fusion of local diagnosis results is carried out by using an adaptive weighted D-S evidence theory. A multi-method integrated diagnosis structure for an engine fault; diagnosis reliability degrees of all fault modes by a local diagnosis systems based on a particle filter and an extreme learning machine are obtained in an off-line mode by using a confusion matrix; and on the basis of the reliability degrees, different weight coefficients are assigned for an evidence body. Therefore, the evidence conflict is reduced effectively and the precision of an integrated diagnosis of a gas path component fault of an engine is improved.

Description

technical field [0001] The invention relates to an aeroengine gas path fault fusion diagnosis method based on self-adaptive weighted D-S evidence theory, which belongs to the field of aeroengine fault diagnosis. Background technique [0002] Aeroengines provide power for aircraft, and their safety and reliability are the key to ensuring flight safety. However, aero-engines have a high failure rate due to their complex mechanical structure and work in harsh environments such as high temperature and high pressure. According to a statistics in the United States, flight accidents caused by engine failure account for about 30% of all accidents. Engine health management technology is an important means to ensure reliable operation of the engine and prevent engine failures. This technology monitors the working status of the engine and diagnoses the failure by using various status information of the engine (maintenance records, sensor measurement data, etc.). Predict the service li...

AI Technical Summary

Problems solved by technology

However, these methods all aim to improve the synthesis rules, but ignore the source of conflicts between the evidence

For the fault fusion diagnosis system of engine gas circuit components, different local diagnosis systems have different diagnostic reliability for each fault mode due to different feature domains and diagnosis mechanisms, resulting in conflicts between different local diagnosis results

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