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首页> 外文期刊>Engineering Structures >A novel step-wise AK-MCS method for efficient estimation of fuzzy failure probability under probability inputs and fuzzy state assumption
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A novel step-wise AK-MCS method for efficient estimation of fuzzy failure probability under probability inputs and fuzzy state assumption

机译：概率输入和模糊状态假设下有效估计模糊失效概率的逐步AK-MCS新方法

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摘要

For efficiently estimating the fuzzy failure probability under the probability inputs and fuzzy state assumption (profust model) which generally includes three states, i.e., the absolute safety state, the full failure state and the fuzzy safety-failure transition state, a novel step-wise AK-MCS method is proposed. In the first step, the Kriging model is adaptively updated by U learning function to accurately recognize if the points in the sample pool are in the safety state or in the failure one, where the exact values of performance function at these points are not concerned in the process of updating the Kriging model. After the Kriging model converges so that all points of the sample pool in the absolute safety state and the fully failure state can be well distinguished, the retained points in the sample pool belong to the fuzzy safety-failure transition state and construct the reduced new sample pool. In the second step, the first converged Kriging model continues to be adaptively updated in the reduced new sample pool. The exact values of the performance function at these points locating in the fuzzy safety-failure transition state are concerned for accurately estimating the fuzzy failure probability. Thus, a global learning function based on the total prediction error is used to select training point in order to update the Kriging model. By using the step-wise strategy and collaborating Kriging surrogates through two-step updating processes with different learning functions, the fuzzy failure probability can be efficiently estimated as a post-processing without any extra calls of the performance function. An automobile front model, a simplified wing box structure model and an icing forecast model are used to illustrate the efficiency and accuracy of the proposed method.
机译：为了在概率输入和模糊状态假设（有效模型）下有效地估计模糊失效概率，该假设通常包括三个状态，即绝对安全状态，完全失效状态和模糊安全失效过渡状态，一种新颖的逐步方法提出了AK-MCS方法。第一步，通过U学习功能对Kriging模型进行自适应更新，以准确识别样品池中的点是处于安全状态还是处于故障状态，而这些点上的性能函数的确切值不受关注更新克里金模型的过程。在Kriging模型收敛之后，可以很好地区分处于绝对安全状态和完全失效状态的样品池的所有点，样品池中的保留点属于模糊安全-失效过渡状态，并构造简化的新样品池。在第二步中，将继续在减少的新样本池中自适应更新第一个聚合Kriging模型。为了准确地估计模糊失效概率，涉及位于安全失效-模糊过渡状态的这些点处的性能函数的精确值。因此，基于总预测误差的全局学习功能用于选择训练点，以更新克里格模型。通过使用逐步策略并通过具有不同学习功能的两步更新过程与Kriging代理协作，可以将模糊故障概率有效地估计为后处理，而无需任何额外的性能函数调用。通过汽车前部模型，简化的翼盒结构模型和结冰预测模型来说明该方法的有效性和准确性。

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  • 来源
    《Engineering Structures》 |2019年第15期|340-350|共11页
  • 作者

    Yun Wanying; Lu Zhenzhou; Feng Kaixuan; Jiang Xian;

  • 作者单位

    Northwestern Polytech Univ, Sch Aeronaut, Xian 710072, Shaanxi, Peoples R China;

    Northwestern Polytech Univ, Sch Aeronaut, Xian 710072, Shaanxi, Peoples R China;

    Northwestern Polytech Univ, Sch Aeronaut, Xian 710072, Shaanxi, Peoples R China;

    Aircraft Flight Test Technol Inst, Chinese Flight Test Estab, Xian 710089, Shaanxi, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Step-wise AK-MCS; Reliability analysis; Fuzzy state assumption; Adaptive Kriging model;

    机译：逐步AK-MCS可靠性分析模糊状态假设自适应克里格模型;

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