Tools and methods for ship system reliability modeling and maintenance decision making

The maritime industry is in a continuous state of digital transformation, and with strong cooperation from all stakeholders—ship designers, classification societies, equipment manufacturers, and shipping companies—fully autonomous ships will become a reality within a limited time. Safety and reliability will be one of the primary challenges, ensuring that equipment and ship systems are in a proper working condition during voyages and that all inspections and repairs will be performed only on shore. For that, ship maintenance, like other industries, needs to change its perspective and move toward a "anticipate and prevent" philosophy using tools and methods to forecast technical failures at an early level for effective logistics planning. This paper presents a new perspective on how reliability analysis like F.T.A, and F.M.E.C.A, and fuzzy set theory can be used together with machine learning and simulation environments to assist maintenance decisions of onboard systems. The proposed approach creates a new model of automatic equipment evaluation with the aim of early signaling of equipment malfunction, elimination of human errors in operation, early notification for future repair, and improve safety.