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首页> 外文期刊>Reliability Engineering & System Safety >EEK-SYS: System reliability analysis through estimation error-guided adaptive Kriging approximation of multiple limit state surfaces
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EEK-SYS: System reliability analysis through estimation error-guided adaptive Kriging approximation of multiple limit state surfaces

机译：EEK-SYS：通过估计误差引导的多个极限状态面的自适应Kriging逼近来进行系统可靠性分析

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摘要

In order to approximate the multiple limit state functions for different failure events, the active learning Kriging model proposed for component reliability analysis has been extended to system reliability analysis. Meanwhile, many efficient sampling strategies have been applied to reduce the high computational burden. However, these strategies meet a challenge in wasting some training points and terminating the training process inappropriately, since they do not directly relate to the estimation error of system failure probability. To address the challenge, this work proposes an estimation error-guided adaptive Kriging method. As Kriging prediction may be inaccurate before being well trained, the predicted system failure probability may deviate from the true result. To quantify this estimation error, the true number of failure points is approximated by adding the number of predicted failure points and the number of wrongly classified points. Since it is impossible to learn the exact number of wrongly classified points, its confidence interval is derived based on the probability of making wrong state classification. Subsequently, the refinement of Kriging is achieved by using the probability to identify new points and using the estimation error to determine the termination, which has been demonstrated by three different cases.
机译：为了近似不同故障事件的多个极限状态函数，为组件可靠性分析提出的主动学习Kriging模型已扩展到系统可靠性分析。同时，已应用许多有效的采样策略来减轻高计算负担。然而，这些策略在浪费一些训练点和不适当地终止训练过程方面遇到了挑战，因为它们与系统故障概率的估计误差没有直接关系。为了解决这一挑战，这项工作提出了一种估计误差指导的自适应克里格方法。由于克里格（Kriging）预测在受到良好训练之前可能不准确，因此预测的系统故障概率可能会偏离真实结果。为了量化此估计误差，可以通过将预测的故障点数和错误分类的点数相加来近似故障点的真实数。由于不可能学习错误分类的点的确切数目，因此，根据做出错误状态分类的概率来得出其置信区间。随后，通过使用概率识别新点并使用估计误差确定终止点来实现对Kriging的改进，这已在三种不同情况下得到了证明。

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