Federated Learning-Aided Prognostics in the Shipping 4.0: Principles, Workflow, and Use Cases

The next generation of shipping industry, namely Shipping 4.0 will integrate advanced
automation and digitization technologies towards revolutionizing the maritime industry. As conventional
maintenance practices are often inefficient, costly, and unable to cope with unexpected failures, leading
to operational disruptions and safety risks, the need for efficient predictive maintenance (PdM), relying
on machine learning (ML) algorithms is of paramount importance. Still, the exchange of training data
might raise privacy concerns of the involved stakeholders. Towards this end, federated learning (FL),
a decentralized ML approach, enables collaborative model training across multiple distributed edge devices,
such as on-board sensors and unmanned vessels and vehicles. In this work, we explore the integration
of FL into PdM to support Shipping 4.0 applications, by using real datasets from the maritime sector.
More specifically, we present the main FL principles, the proposed workflow and then, we evaluate and
compare various FL algorithms in three maritime use cases, i.e. regression to predict the naval propulsion
gas turbine (GT) measures, classification to predict the ship engine condition, and time-series regression
to predict ship fuel consumption. The efficiency of the proposed FL-based PdM highlights its ability
to improve maintenance decision-making, reduce downtime in the shipping industry, and enhance the
operational efficiency of shipping fleets. The findings of this study support the advancement of PdM
methodologies in Shipping 4.0, providing valuable insights for maritime stakeholders to adopt FL, as a
viable and privacy-preserving solution, facilitating model sharing in the shipping industry and fostering
collaboration opportunities among them.