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Probabilistic framework to evaluate the resilience of engineering systems using Bayesian and dynamic Bayesian networks

机译：使用贝叶斯网络和动态贝叶斯网络评估工程系统弹性的概率框架

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摘要

Resilience indicators are a convenient tool to assess the resilience of engineering systems. They are often used in preliminary designs or in the assessment of complex systems. This paper introduces a novel approach to assess the time-dependent resilience of engineering systems using resilience indicators. A Bayesian network (BN) approach is employed to handle the relationships among the indicators. BN is known for its capability of handling causal dependencies between different variables in probabilistic terms. However, the use of BN is limited to static systems that are in a state of equilibrium. Being at equilibrium is often not the case because most engineering systems are dynamic in nature as their performance fluctuates with time, especially after disturbing events (e.g. natural disasters). Therefore, the temporal dimension is tackled in this work using the Dynamic Bayesian Network (DBN). DBN extends the classical BN by adding the time dimension. It permits the interaction among variables at different time steps. It can be used to track the evolution of a system's performance given an evidence recorded at a previous time step. This allows predicting the resilience state of a system given its initial condition. A mathematical probabilistic framework based on the DBN is developed to model the resilience of dynamic engineering systems. Two illustrative examples are presented in the paper to demonstrate the applicability of the introduced framework. One example evaluates the resilience of Brazil. The other one evaluates the resilience of a transportation system.
机译：弹性指标是评估工程系统弹性的便捷工具。它们通常用于初步设计或复杂系统的评估中。本文介绍了一种使用弹性指标评估工程系统随时间变化的弹性的新颖方法。贝叶斯网络（BN）方法用于处理指标之间的关系。 BN以其以概率术语处理不同变量之间的因果关系的能力而闻名。但是，BN的使用仅限于处于平衡状态的静态系统。通常情况并非如此，因为大多数工程系统的性能是随时间变化的，因此大多数工程系统本质上都是动态的，尤其是在发生干扰事件（例如自然灾害）之后。因此，使用动态贝叶斯网络（DBN）解决了时间维问题。 DBN通过添加时间维度来扩展经典BN。它允许变量在不同时间步长之间进行交互。给定在先前时间步长记录的证据，它可用于跟踪系统性能的演变。在给定初始条件的情况下，这可以预测系统的弹性状态。开发了基于DBN的数学概率框架来对动态工程系统的弹性建模。本文中提供了两个说明性示例，以演示引入的框架的适用性。一个例子评估了巴西的弹性。另一个评估运输系统的弹性。

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