Autonomous Structural Integrity Assessment and Predictive Maintenance of Subsea Escape Capsules via Multi-Modal Data Fusion and Bayesian Network Inference

**Abstract:** This paper proposes a novel methodology for autonomously assessing the structural integrity and predicting maintenance needs of subsea escape capsules. Leveraging multi-modal sensor data (acoustic emission, visual inspection, strain gauges) coupled with Bayesian Network inference, our system dynamically models capsule degradation and facilitates proactive maintenance scheduling, ultimately enhancing crew safety and minimizing operational downtime in harsh marine environments. The system aims to provide a 30% reduction in unscheduled maintenance and a quantifiable improvement in capsule lifespan prediction accuracy compared to existing periodic inspection-based methods. This research builds upon the established field of structural health monitoring (SHM) but introduces a unique data fusion and probabilistic reasoning framework tailored specifically to the complex operational dynamics of subsea escape capsules. Rigorous simulations and preliminary experimental data demonstrate the feasibility and efficacy of this approach.