A Bayesian approach is often applied when updating a deterioration model using observations from structural health monitoring or condition monitoring. Usually, observations are assumed to be independent conditioned on the damage size, 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 independent. This paper presents a Bayesian network modeling approach for inclusion of temporal dependency between measurements through a dependency parameter and presents a generic monitoring model based on 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. An example and a 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 can be obtained when the dependency is properly modeled. In case of temporally dependent observations, an advanced decision model using a digital twin for decision making is needed to make monitoring feasible compared to only using inspections.
|Tidsskrift||Structural Health Monitoring|
|Status||Afsendt - 2020|