Fatigue reliability framework using enhanced active Kriging-based hierarchical collaborative strategy

doi:10.1108/IJSI-09-2022-0116

2023-02-06

PRE

DOI：10.1108/IJSI-09-2022-0116

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摘要

En 中文

PurposeThe purpose of this study is to improve the computational efficiency and accuracy of fatigue reliability analysis.Design/methodology/approachBy absorbing the advantages of Markov chain and active Kriging model into the hierarchical collaborative strategy, an enhanced active Kriging-based hierarchical collaborative model (DCEAK) is proposed.FindingsThe analysis results show that the proposed DCEAK method holds high accuracy and efficiency in dealing with fatigue reliability analysis with high nonlinearity and small failure probability.Research limitations/implicationsThe effectiveness of the presented method in more complex reliability analysis problems (i.e. noisy problems, high-dimensional issues etc.) should be further validated.Practical implicationsThe current efforts can provide a feasible way to analyze the reliability performance and identify the sensitive variables in aeroengine mechanisms.Originality/valueTo improve the computational efficiency and accuracy of fatigue reliability analysis, an enhanced active DCEAK is proposed and the corresponding fatigue reliability framework is established for the first time.

Keyword:

Fatigue reliability

High cycle fatigue

Kriging model

Active learning