System Reliability Analysis With Autocorrelated Kriging Predictions
System Reliability Analysis With Autocorrelated Kriging Predictions
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DOI：
10.1115/1.4046648
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发表时间：
2020-05
影响因子：
3.3
通讯作者：
Hao Wu;Zhifu Zhu;Xiaoping Du
中科院分区：
文献类型：
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作者：
Hao Wu;Zhifu Zhu;Xiaoping Du
当极限状态函数是高度非线性时，传统的可靠度方法，如一阶和二阶可靠度方法，是不准确的。另一方面，如果使用足够的样本量，蒙特卡罗模拟（MCS）是准确的，但计算密集。本研究提出一种新的系统可靠性方法，结合MCS和克里格方法，提高精度和效率。精确的代理模型创建的极限状态函数的最小方差的系统可靠性的估计，从而产生高精度的系统可靠性预测。该方法不采用全局优化，而是使用MCS样本，从中选择代理模型的训练点。该方法通过考虑代理模型的自相关性，更准确地捕捉到每个MCS样本对串联系统可靠性估计中不确定性的贡献，从而有效地选择训练点。通过四个实例证明了该方法的准确性和有效性。
When limit-state functions are highly nonlinear, traditional reliability methods, such as the first-order and second-order reliability methods, are not accurate. Monte Carlo simulation (MCS), on the other hand, is accurate if a sufficient sample size is used but is computationally intensive. This research proposes a new system reliability method that combines MCS and the Kriging method with improved accuracy and efficiency. Accurate surrogate models are created for limit-state functions with minimal variance in the estimate of the system reliability, thereby producing high accuracy for the system reliability prediction. Instead of employing global optimization, this method uses MCS samples from which training points for the surrogate models are selected. By considering the autocorrelation of a surrogate model, this method captures the more accurate contribution of each MCS sample to the uncertainty in the estimate of the serial system reliability and therefore chooses training points efficiently. Good accuracy and efficiency are demonstrated by four examples.