Marine corrosion significantly reduces a ship's availability, increases costs of operation and could impact safety. Protective coatings mitigate these risks, but their effectiveness deteriorates over time. Early detection of coating breakdown is crucial to prevent costly repairs and safety concerns. While corrosion itself is well-understood, coating degradation remains under-investigated due to insufficient long-term data. This work addresses this knowledge gap by enhancing coating defect prediction and optimizing inspection planning for ships. The Power Law Non-Homogeneous Poisson Process (PL-NHPP) is utilized for modeling coating defect arrivals. Unlike prior studies, we employ a hierarchical Bayesian approach for parameter fitting, effectively addressing limitations associated with scarce real-world data. Furthermore, we optimize inspection planning by incorporating out-of-service costs and potential costs increases due to delayed repairs. The efficacy of these methods is evaluated through a comprehensive case study involving a recently commissioned fleet with limited historical data. This research contributes to the advancement of condition-based maintenance (CBM) strategies for ships by enabling more accurate prediction of coating breakdowns and optimizing inspection schedules early in the life of the fleet. This approach ultimately improves operational efficiency and reduces life-cycle costs.

翻译：船舶腐蚀显著降低其可用性，增加运营成本，并可能影响安全性。防护涂层可缓解这些风险，但其有效性会随时间推移而下降。早期检测涂层失效对于避免高昂维修费用和安全问题至关重要。尽管腐蚀现象本身已被充分研究，但涂层退化因缺乏长期数据而研究不足。本研究通过增强涂层缺陷预测和优化船舶检验规划来填补这一知识空白。采用幂律非齐次泊松过程（PL-NHPP）对涂层缺陷到达进行建模。与以往研究不同，我们采用分层贝叶斯方法进行参数拟合，有效解决了实际数据稀缺带来的局限性。此外，我们通过纳入停航成本和延迟维修导致的潜在成本增加来优化检验规划。通过一项涉及近期投入运营且历史数据有限的船队的综合案例研究，评估了这些方法的有效性。本研究通过实现更准确的涂层失效预测和优化船舶运营初期的检验计划，推动了船舶状态维修（CBM）策略的发展。该方法最终提高了运营效率并降低了全生命周期成本。