Automated Structural Health Monitoring and Predictive Maintenance for FPSO Hoses using Deep Reinforcement Learning and HyperScore Evaluation

**Abstract:** This paper introduces a novel, automated system for Structural Health Monitoring (SHM) and Predictive Maintenance (PdM) of Flexible Risers and Mooring Hoses in Floating Production Storage and Offloading (FPSO) vessels. Traditional methods rely on periodic, manual inspections, proving costly and potentially inaccurate. Our system, integrating Deep Reinforcement Learning (DRL), advanced signal processing, and a newly defined HyperScore evaluation metric, achieves real-time hose assessment, predicting failures with unprecedented accuracy and enabling proactive maintenance, leading to significant cost savings and improved operational safety. The key innovation lies in a closed-loop system where DRL agents autonomously optimize sensor placement and inspection scheduling while the HyperScore provides a quantitative assessment of hose health, guaranteeing reliability and scalability based on data-driven insights.