Predictive Health Analysis for Future Maintenance Planning in Aging Containership Hull Structures Within Digital Healthcare Engineering Systems

Abstract Aging ships and offshore structures face significant risks from corrosion, fatigue cracking, and mechanical damage, worsened by harsh marine environments and remote operations. Ensuring their safety and sustainability requires innovative solutions, leveraging automated technologies, digital solutions and advanced communication systems. This paper introduces the Digital Healthcare Engineering (DHE) system, a proactive, real-time monitoring and artificial intelligence (AI)-driven framework for managing the structural health of aging vessels and the well-being of seafarers. The AI-enhanced DHE system includes five modules: (1) Module 1: On-site real-time monitoring and digitalization of structural health parameters, (2) Module 2: Transmission of collected data to a land-based analytics center via low Earth orbit (LEO) satellites, (3) Module 3: Advanced analytics and simulations through digital twin technology, (4) Module 4: AI-driven diagnostics with automated maintenance recommendations, and (5) Module 5: Predictive health analysis for future maintenance planning. This study focuses on Module 5, which uses damage data to predict corrosion wastage and fatigue crack propagation, assess structural strength reduction, and optimize maintenance schedules. A case study on a hypothetical 25,800 TEU containership powered by small modular reactors (SMRs) demonstrates the system’s practical benefits in enhancing the safety and operational sustainability.