A Bayesian approach is often applied when updating a deterioration model using observations from inspections, structural health monitoring, or condition monitoring. The observations are stochastic variables with probability distributions that depend on the damage size. Consecutive observations are usually assumed to be independent of each other, but this assumption does not always hold, especially when using online monitoring systems. Frequent updating using dependent measurements can lead to an over-optimistic assessment of the value of information when the measurements are incorrectly modeled as being independent. This article presents a Bayesian network modeling approach for the inclusion of temporal dependency between measurements through a dependency parameter and presents a generic monitoring model based on the exceedance of thresholds for a damage index. Additionally, the model is implemented in a computational framework for risk-based maintenance planning, developed for maintenance planning for wind turbines. The framework is applied for a numerical experiment, where the expected lifetime costs are found for strategies with monitoring with and without dependency between observations, and also for the case where dependency is present but is neglected when making decisions. The numerical experiment and associated parameter study show that neglecting dependency in the decision model when the observations are in fact dependent can lead to much higher costs than expected and to the selection of non-optimal strategies. Much lower costs (down to one quarter) can be obtained when the dependency is properly modeled. In the case of temporally dependent observations, an advanced decision model using a Bayesian network as a simple digital twin is needed to make monitoring feasible compared to only using inspections.

在使用来自检查、结构健康监测或状态监测的观察更新退化模型时，通常会应用贝叶斯方法。观测值是随机变量，其概率分布取决于损伤大小。通常假设连续观察是相互独立的，但这种假设并不总是成立，尤其是在使用在线监测系统时。当测量被错误地建模为独立时，使用相关测量进行频繁更新会导致对信息价值的过度乐观评估。本文提出了一种贝叶斯网络建模方法，用于通过依赖参数包含测量之间的时间依赖，并提出了一种基于超出损坏指数阈值的​​通用监控模型。此外，该模型是在基于风险的维护计划的计算框架中实现的，该框架是为风力涡轮机的维护计划而开发的。该框架应用于数值实验，其中预期的生命周期成本可用于在观察之间具有和不具有依赖性的监测策略，以及存在依赖性但在做出决策时被忽略的情况。数值实验和相关参数研究表明，当观测值实际上是相关的时，忽略决策模型中的相关性会导致比预期高得多的成本和非最优策略的选择。当依赖关系被正确建模时，可以获得更低的成本（低至四分之一）。在时间相关观察的情况下，与仅使用检查相比，需要使用贝叶斯网络作为简单数字孪生的高级决策模型才能使监测变得可行。