Dynamic maintenance planning for autonomous marine systems (AMS) and operations

Compared to conventional marine systems, where the onboard crew can perform frequent and flexible maintenance, autonomous marine systems (AMS) only involve a limited (or, even no) crew during a voyage, and this challenges maintenance planning and execution. The current study identifies the relevant issues and proposes to solve these through developing a dynamic maintenance planning method for AMS. By considering economical dependencies among components, the study presents a dynamic grouping method to determine the optimum maintenance opportunities for AMS in the future. Stochastic dependencies of components are considered by using the Markov model. A multiphase Markov model is proposed for modeling stochastic dependencies between components where the limited and irregular maintenance opportunities are handled by the multiphase part of the model. A heuristic method is proposed to deal with the combinatorial challenge. To demonstrate the application of the proposed method, the maintenance planning of a cooling system of an autonomous ship is performed in a case study.To validate its performance, the proposed heuristic method is compared with existing 'short-sighted' methods for a selection of candidate groups for maintenance. In the validation, various scenarios with different component states and maintenance strategies are tested.

Automated summary

Compared to conventional marine systems, autonomous marine systems face challenges in maintenance planning and execution due to limited or no crew involvement during a voyage. This study proposes a dynamic maintenance planning method for AMS by considering economical dependencies among components and using a multiphase Markov model to account for stochastic dependencies. A heuristic method is introduced to address the combinatorial challenge. The proposed method is applied to the maintenance planning of a cooling system in an autonomous ship, and its performance is validated against existing methods in various scenarios.