Structural Health Monitoring (SHM) describes a process for inferring quantifiable metrics of structural condition, which can serve as input to support decisions on the operation and maintenance of infrastructure assets. Given the long lifespan of critical structures, this problem can be cast as a sequential decision making problem over prescribed horizons. Partially Observable Markov Decision Processes (POMDPs) offer a formal framework to solve the underlying optimal planning task. However, two issues can undermine the POMDP solutions. Firstly, the need for a model that can adequately describe the evolution of the structural condition under deterioration or corrective actions and, secondly, the non-trivial task of recovery of the observation process parameters from available monitoring data. Despite these potential challenges, the adopted POMDP models do not typically account for uncertainty on model parameters, leading to solutions which can be unrealistically confident. In this work, we address both key issues. We present a framework to estimate POMDP transition and observation model parameters directly from available data, via Markov Chain Monte Carlo (MCMC) sampling of a Hidden Markov Model (HMM) conditioned on actions. The MCMC inference estimates distributions of the involved model parameters. We then form and solve the POMDP problem by exploiting the inferred distributions, to derive solutions that are robust to model uncertainty. We successfully apply our approach on maintenance planning for railway track assets on the basis of a "fractal value" indicator, which is computed from actual railway monitoring data.
翻译:结构性健康监测(SHM)描述了一个推断结构性状况可计量指标的过程,它可以作为支持基础设施资产运行和维护决策的投入。鉴于关键结构的寿命长,这一问题可以作为一个超越规定视野的顺序决策问题。部分可观测的Markov决策程序(POMDPs)提供了一个正式框架,以解决基本的最佳规划任务。然而,两个问题可能会破坏POMDP解决方案。第一,需要一种模型,能够充分描述恶化或纠正行动下结构状况的演变,第二,从现有监测数据中恢复观察过程参数的非三重任务。尽管存在这些潜在挑战,但所采用的POMDP模型通常不会说明模型参数的不确定性,导致不切实际的解决方案。在这项工作中,我们提出了一个框架,用以评估POMDP的过渡和观察模型参数,直接从现有数据模型中,通过Markov链 Monte Carlo(MC)对隐性Markov模型(HMM)的取样,然后以行动为条件。我们通过MMMC对数字模型进行精确的估算,我们用模型对数据分布进行了成功的评估,我们用模型的方法将数据推算出了不确定性的模型。