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Reliability analysis method of coupling optimal importance sampling density and multi-fidelity Kriging model
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Online publication date: 2023-03-15
Publication date: 2023-03-15
Eksploatacja i Niezawodność – Maintenance and Reliability 2023;25(2):161893
  • Proposed a learning function for multi-fidelity models.
  • A practical reliability calculation stopping condition is proposed.
  • Proposed a feasible and applicable structural reliability analysis framework for multifidelity models.
The commonly used reliability analysis approaches for Kriging-based models are usually conducted based on high-fidelity Kriging models. However, high-fidelity surrogate models are commonly costly. Therefore, in order to balance the calculation expense and calculation time of the surrogate model, this paper proposes a multi-fidelity Kriging model reliability analysis approach with coupled optimal important sampling density (OISD+MFK). First, the MEI learning function is proposed considering the training sample distance, model computation cost, expected improvement function, and model relevance. Second, a dynamic stopping condition is proposed that takes into account the failure probability estimation error. Finally, the optimal importance sampling density is incorporated into the reliability analysis process, which can effectively reduce failure probability estimation error. The results of the study show that the approach proposed in this paper can reduce the calculation cost while outputting relatively accurate failure probability evaluation results.
An active learning reliability algorithm using DMSSA‐optimized Kriging model and parallel infilling strategy
Yuhang Sun, Zhijie Liu, Pengpeng Zhi, Xiaobang Wang, Junjie Wang
Quality and Reliability Engineering International
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