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A dynamic discretization method for reliability inference in Dynamic Bayesian Networks

机译：动态贝叶斯网络中可靠性推理的动态离散化方法

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The material and modeling parameters that drive structural reliability analysis for marine structures are subject to a significant uncertainty. This is especially true when time-dependent degradation mechanisms such as structural fatigue cracking are considered. Through inspection and monitoring, information such as crack location and size can be obtained to improve these parameters and the corresponding reliability estimates. Dynamic Bayesian Networks (DBNs) are a powerful and flexible tool to model dynamic system behavior and update reliability and uncertainty analysis with life cycle data for problems such as fatigue cracking. However, a central challenge in using DBNs is the need to discretize certain types of continuous random variables to perform network inference while still accurately tracking low-probability failure events. Most existing discretization methods focus on getting the overall shape of the distribution correct, with less emphasis on the tail region. Therefore, a novel scheme is presented specifically to estimate the likelihood of low-probability failure events. The scheme is an iterative algorithm which dynamically partitions the discretization intervals at each iteration. Through applications to two stochastic crack-growth example problems, the algorithm is shown to be robust and accurate. Comparisons are presented between the proposed approach and existing methods for the discretization problem. (C) 2015 Elsevier Ltd. All rights reserved.
机译：用于驱动海洋结构的结构可靠性分析的材料和建模参数存在很大的不确定性。当考虑到时间相关的退化机制（例如结构疲劳裂纹）时，尤其如此。通过检查和监视，可以获得裂缝位置和大小等信息，以改善这些参数和相应的可靠性估计。动态贝叶斯网络（DBN）是一种功能强大且灵活的工具，可以对动态系统行为进行建模，并使用生命周期数据更新可靠性和不确定性分析，以解决诸如疲劳裂纹等问题。但是，使用DBN的主要挑战是需要离散化某些类型的连续随机变量以执行网络推理，同时仍要准确地跟踪低概率故障事件。大多数现有的离散化方法都专注于使分布的总体形状正确，而较少关注尾部区域。因此，专门提出了一种新颖的方案来估计低概率故障事件的可能性。该方案是一种迭代算法，可在每次迭代时动态划分离散化间隔。通过对两个随机裂纹扩展实例问题的应用，该算法被证明是鲁棒且准确的。比较了提出的方法和现有方法的离散化问题。 （C）2015 Elsevier Ltd.保留所有权利。

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