Mooring systems that are used to secure position keeping of floating offshore oil and gas facilities are subject to deterioration processes, such as pitting corrosion and fatigue crack growth. Past investigations show that pitting corrosion has a significant effect on reducing the fatigue resistance of mooring chain links. In situ inspections are essential to monitor the development of the corrosion condition of the components of mooring systems and ensure sufficient structural safety. Unfortunately, offshore inspection campaigns require large financial commitments. As a consequence, inspecting all structural components is unfeasible. This article proposes to use value of information analysis to rank identified inspection alternatives. A Bayesian Network is proposed to model the statistical dependence of the corrosion deterioration among chain links at different locations of the mooring system. This is used to efficiently update the estimation of the corrosion condition of the complete mooring system given evidence from local observations and to reassess the structural reliability of the system. A case study is presented to illustrate the application of the framework.

用于确保海上浮动油气设施的位置保持的系泊系统会受到点蚀和疲劳裂纹扩展等恶化过程的影响。过去的研究表明，点蚀对降低系泊链节的抗疲劳性具有显著作用。现场检查对于监视系泊系统组件腐蚀状况的发展并确保足够的结构安全至关重要。不幸的是，海上检查活动需要大量的财务承诺。结果，检查所有结构部件是不可行的。本文建议利用信息分析的价值对已确定的检查备选方案进行排名。提出了一种贝叶斯网络来对系泊系统不同位置的链节之间腐蚀恶化的统计依赖性进行建模。根据本地观察的证据，这可用于有效地更新整个系泊系统腐蚀状况的估计，并重新评估系统的结构可靠性。提出了一个案例研究来说明该框架的应用。