Abstract
Risk Based Inspection (RBI) planning for offshore floating structures optimizes inspections and maintenance costs by managing risks related to degradation. This paper presents an innovative approach integrating live sensor data and digital twin technology into the RBI planning process for Floating Production Storage and Offloading (FPSO) units. Developed by DNV and SBM Offshore, the methodology covers key degradation mechanisms such as fatigue, corrosion, and coating degradation. Real-time operational data dynamically updates inspection plans, ensuring timely identification of potential issues and optimized maintenance activities. Endorsed by DNV, ABS and BV class societies, this approach improves the structural integrity and operational efficiency of FPSO units and is adaptable to other offshore structures. Future improvements could include enhanced data analytics and machine learning algorithms for predictive maintenance.
