Optimal inspection and maintenance planning for deteriorating structural components through dynamic Bayesian networks and Markov decision processes
Optimal inspection and maintenance planning for deteriorating structural components through dynamic Bayesian networks and Markov decision processes
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DOI：
10.1016/j.strusafe.2021.102140
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发表时间：
2021-10-30
影响因子：
5.8
通讯作者：
Rigo, P.
中科院分区：
文献类型：
--
作者：
Morato, P. G.;Papakonstantinou, K. G.;Rigo, P.
土木和海事工程系统（从桥梁到海上平台和风力涡轮机）必须得到有效管理，因为它们在整个使用寿命期间都面临着老化机制，例如疲劳和/或腐蚀。确定最佳的检查和维护策略需要解决不确定性下的复杂顺序决策问题，其主要目标是有效控制与结构故障相关的风险。为了解决这种复杂性，基于风险的检查规划方法（通常由动态贝叶斯网络支持）评估一组预定义的启发式决策规则，以合理地简化决策问题。然而，最终的策略可能会受到决策规则定义中考虑的有限空间的影响。为了避免这种限制，部分可观察马尔可夫决策过程（POMDP）为不确定行动结果和观察下的随机最优控制提供了一种原则性的数学方法，其中最优行动被规定为整个动态更新的状态概率分布的函数。在本文中，我们将动态贝叶斯网络与 POMDP 结合在一个联合框架中，以实现最佳检查和维护规划，并提供了在结构可靠性背景下开发无限和有限水平 POMDP 的相关公式。所提出的方法在遭受疲劳恶化的结构部件的情况下进行了实施和测试，展示了最先进的基于点的 POMDP 求解器解决底层规划随机优化问题的能力。在数值实验中，对 POMDP 和基于启发式的策略进行了彻底比较，结果表明，即使对于传统的问题设置，POMDP 也比同类策略实现了更低的成本。
Civil and maritime engineering systems, among others, from bridges to offshore platforms and wind turbines, must be efficiently managed, as they are exposed to deterioration mechanisms throughout their operational life, such as fatigue and/or corrosion. Identifying optimal inspection and maintenance policies demands the solution of a complex sequential decision-making problem under uncertainty, with the main objective of efficiently controlling the risk associated with structural failures. Addressing this complexity, risk-based inspection planning methodologies, supported often by dynamic Bayesian networks, evaluate a set of pre-defined heuristic decision rules to reasonably simplify the decision problem. However, the resulting policies may be compromised by the limited space considered in the definition of the decision rules. Avoiding this limitation, Partially Observable Markov Decision Processes (POMDPs) provide a principled mathematical methodology for stochastic optimal control under uncertain action outcomes and observations, in which the optimal actions are prescribed as a function of the entire, dynamically updated, state probability distribution. In this paper, we combine dynamic Bayesian networks with POMDPs in a joint framework for optimal inspection and maintenance planning, and we provide the relevant formulation for developing both infinite and finite horizon POMDPs in a structural reliability context. The proposed methodology is implemented and tested for the case of a structural component subject to fatigue deterioration, demonstrating the capability of state-of-the-art point-based POMDP solvers of solving the underlying planning stochastic optimization problem. Within the numerical experiments, POMDP and heuristic-based policies are thoroughly compared, and results showcase that POMDPs achieve substantially lower costs as compared to their counterparts, even for traditional problem settings.