ABSTRACT
In conventional industrial practices, the structural performance is evaluated against deterioration mechanisms as well as extreme and/or accidental events. However, most existing approaches treat the aforementioned structural assessments separately. In this work, we identify system inspection and maintenance strategies by jointly modeling deterioration processes and accidental/extreme events within the overall life-cycle management optimization. In particular, the decision-making objective is formulated considering a system failure risk metric that depends on the damage caused by deterioration processes and accidental/extreme hazards. In order to enable efficient inference and uncertainty propagation, we propose an underlying probabilistic approach relying on dynamic Bayesian networks. The efficacy of the proposed approach is tested in a life-cycle management setting where the risk of an offshore wind frame substructure is controlled by timely allocating inspection and maintenance actions. Within the investigation, we also observe the influence of ship collision events frequency on the resulting asset management strategies.
