Active reliability analyzing and evaluating method for highly-reliable mechanical products

Abstract

The invention provides an active reliability analyzing and evaluating method for highly-reliable mechanical products. The active reliability analyzing and evaluating method for the highly-reliable mechanical products includes: firstly, establishing ultimate state functions of mechanical products, utilizing a Monte Carlo simulation method to produce sample points conforming to parameter distribution, utilizing a Kriging meta-model to simulate the ultimate state functions, improving simulation precision and confirming an optimal sampling radius by automatically updating DOE (design of experiment), constructing important sampling density functions, producing random sample points through the important sampling density functions, updating the DOE continually, then extracting radius random sample points obeying chi-square distribution, confirming failure probability and failure coefficient of variation of the mechanical products, and limiting whether updating finishes or not by the failure coefficient of variation so as to obtain reliability of the mechanical products finally. The active reliability analyzing and evaluating method for the highly-reliable mechanical products has the advantages of high efficiency, good robustness and high simulation precision, comprehensive design and analysis of rail wear life and reliability to spatial mechanisms are achieved, and the active reliability analyzing and evaluating method for the highly-reliable mechanical products is significant to reliability design technology engineering of complex mechanisms of aerospace equipment.

Description

technical field [0001] The invention relates to an active reliability analysis and evaluation method for high-reliability mechanical products, which combines Kriging (Kriging method) and important sampling outside the radius, and belongs to the technical research field of mechanical structure reliability. Background technique [0002] With the development of mechanical products in the direction of high precision, light weight and high performance, highly reliable mechanical products are becoming the mainstream today, but the primary difficulty facing the reliability design, evaluation and verification of mechanical products is the lack of data and related high reliability quantitative There is a gap in design and verification technology. In addition, mechanical products are complex and have various functions. The coupling between functions causes the limit state function of mechanical product failure to be highly nonlinear. Therefore, the difficulty of reliability analysis of...

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Problems solved by technology

At present, the stochastic response surface method (SRSM) has developed into an important research direction for simulating implicit limit state functions, but for highly nonlinear limit state functions and small failure probability events, SRSM has certain limitations.

The Kriging method considers the spatial correlation of the model error on the basis of the regression polynomial, and is applicable to the reliability calculation of the limit state function with complex shapes and high nonlinearity. However, the Kriging method related parameter optimization problem has not been well solved so far. , in practice, the calculation accuracy of structural reliability simulation based on Kriging is not stable, which limits its further development in the field of structural reliability analysis. Therefore, it is necessary to use Kriging's local predicted value and local variance data to construct an active learning function, and use the learning function to update DOE (Design of Experiments) to judge the accuracy of Kriging's simulated limit state function and reduce reliability calculation errors

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