Gas channel measurement parameter selecting method applied to engine gas channel analysis

Abstract

The invention discloses a gas channel measurement parameter selecting method applied to engine gas channel analysis. The gas channel measurement parameter selecting method applied to engine gas channel analysis includes the steps that gas channel measurement parameters and health parameters are determined according to a working state of an engine; a first sensibility influence coefficient array H of the gas channel measurement parameters and the health parameters is obtained; a second influence coefficient array S is generated; sensibility of each health parameter corresponding to each gas channel measurement parameter is obtained; fuzzy nearness among all the gas channel measurement parameters is obtained, and a measurement parameter nearness array R is formed; an overall relative coefficient beta is calculated; multiple groups of class group data are formed; a health parameter nearness array E is formed; an overall redundancy coefficient psi is calculated; the optimal class group data are screened out; the steps are repeated to obtain the optimal class group data of the engine in all working states. By means of the method, a gas channel measurement parameter optimum selection scheme can be determined fast.

Description

technical field [0001] The invention relates to the technical field of engine gas path analysis, in particular to a gas path measurement parameter selection method applied to engine gas path analysis. Background technique [0002] In the prior art, the optimization and selection technology of gas path measurement parameters for aero-engine gas path analysis has achieved a certain range of applications in the field of aero-engines and gas turbines, such as the PYTHIA gas path analysis software used in Clayfield University (Note: approximately approx. In 2009-2010, the British patent was obtained, but the patent number was not retrieved) and GE90, Trent-800, Trent-900 and other engines. [0003] The existing methods are mainly divided into the following aspects: analysis methods based on influence coefficients, optimization methods based on heuristic / artificial intelligence algorithms, and state observability analysis methods. The above three methods all have their own shortc...

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Problems solved by technology

[0006] This type of method is scientific and reliable, easy to understand and operate, but it has the following two shortcomings: First, in the process of parameter selection, it is necessary to combine the experience with the calculated parameter selection results, which cannot achieve intelligent optimization, resulting in the selected gas path The combination of measurement parameters may not be the optimal solution; secondly, this type of method does not consider the impact of measurement uncertainty

[0013] (1) The previous methods of this kind were only based on one aspect or several considerations (such as sensitivity analysis), did not comprehensively consider all aspects of gas path analysis, and could not achieve multi-objective optimization based on mathematical algorithms, resulting in practical In engineering applications, the results are distorted and the accuracy is low;

[0014] (2) Since the previous methods did not realize automatic dynamic intelligent selection of measurement parameters, it was difficult to integrate them into design systems such as gas path analysis system software and health management systems;

[0015] (3) Traditional methods are often limited to the selection of gas path measurement parameters in the steady state of the engine, and the parameter selection results are not necessarily applicable to all working states of the engine

Therefore, large errors are often caused, and it is not conducive to the dynamic intelligent optimization selection of measurement parameters;

[0016] (4) In the previous methods, the correlation analysis and classification of measurement parameters are not scientific and reasonable, often rely on certain manual experience judgments, and do not make full use of artificial intelligence for optimization selection, which affects the efficiency and accuracy of gas path analysis ;

[0017] (5) The previous methods of this kind did not take into account the diffusion effect (Smearing Effect) of the gas path analysis (mainly the gas path fault diagnosis) in the results of the measurement parameter selection, and did not make an in-depth analysis of this aspect, resulting in the use of the selected measurement parameters Although the accuracy requirements are met, there is a large diffusion effect in the gas path analysis results;

[0018] In addition, such methods usually need to use the engine performance simulation model as the basis and premise, and have a high degree of dependence on the simulation model. Different simulation models will lead to large differences in results and accuracy, which hinders the introduction of this technology in China. application poses great difficulties

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