Reliability simulation method related to failure of multiple components of typical mechanism of aero-engine

Abstract

The invention discloses a reliability simulation method related to failure of multiple components of a typical mechanism of an aero-engine. The method comprises the following steps: firstly, determining an initial state of a Markov chain based on an active learning kriging model, and then identifying all most probable failure domains in a multi-failure performance function according to Markov chain Monte Carlo simulation; then constructing an adaptive kernel density estimation important sampling function through samples in the failure domain, taking important samples in the adaptive reduced sample pool as candidate samples, and adding new sample points through a learning function to update the proxy model until the proxy model approaches a real performance function; and finally, calculating a failure probability by utilizing the finally updated proxy model. The Markov chain initial state determination method and the adaptive sample pool reduction strategy provided by the invention are combined with the active learning Kriging proxy model, so that the modeling precision is improved, the number of candidate samples is reduced, and the calculation precision and the calculation efficiency are relatively high.

Description

technical field [0001] The invention belongs to the technical field of aero-engine reliability analysis and evaluation, and in particular relates to a reliability simulation method related to the failure of multiple components of a typical mechanism of an aero-engine. Background technique [0002] The number of aero-engine mechanism components is large, and the assembly relationship of each component is complex. Under harsh service conditions, due to the influence of load environment, structural design tolerance, material performance parameters, clearance fit tolerance and other factors, multi-component failure is prone to occur, which seriously affects the Institutional reliability and security. In addition, due to the multiple uncertainties of material parameters, load parameters and model parameters, the component life presents a large dispersion. At present, there are many studies on reliability theory and methods, but there are few studies on reliability simulation met...

AI Technical Summary

Problems solved by technology

However, most of the existing methods determine the initial state of the Markov chain through numerical analysis or engineering experience, which is highly subjective.

For complex highly nonlinear engineering problems, it is very difficult to subjectively determine the initial state

At the same time, traditional importance sampling methods usually use the entire importance sampling sample as a candidate sample, and the large sample size seriously affects the computational efficiency

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