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 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 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 for solving the underlying planning 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.
翻译:除其他外,从桥梁到近海平台和风力涡轮机等民用和海洋工程系统必须受到有效管理,因为它们在整个运作期内都面临疲劳或腐蚀等退化机制; 确定最佳检查和维护政策要求解决不确定的复杂顺序决策问题,主要目标是有效控制与结构性故障有关的风险; 解决这一复杂问题,基于风险的检查规划方法,往往得到动态的巴耶西亚网络的支持,评估一套预先界定的超常决策规则,以合理简化决策问题; 然而,由此产生的政策可能由于决定规则定义中考虑的空间有限而受到损害; 避免这一限制,部分可观测的马克夫决策程序(部分可观测的马可决定程序)提供了一种原则性数学方法,以便在不确定的行动结果和观察下,以有效控制与结构性故障相关的风险; 处理这一复杂问题的最佳行动是整个、动态更新和状态概率分布问题; 在本文件中,我们将动态的巴耶斯网络与基于公私伙伴关系的解决方案合并为一个最佳检查和维护规划的联合框架,我们为制定不限值和固定地平价的马可操作决策程序提供了设计框架; 在结构上,其结构上,为结构上对结构性改革进行成本分析,并测试; 提出,为结构上压压压压力,为结构的解决方案的解决方案进行成本分析,为结构分析,为结构的升级进行测试,为结构的升级的升级进行测试,以测试,为内部的升级的升级,以测试,以测试,为结构分析,为结构的流程进行成本。