Marine dual fuel engines monitoring in the wild through weakly supervised data analytics

摘要

Background: Maritime transportation accounts for around 80% of the world freight movements, remarkably contributing to the global environmental footprint. Dual fuel engines, running on both gaseous and liquid fuels, represent a viable way toward the reduction of emissions at the cost of additional complexity in monitoring activities. Motivation: Data-driven methods represent the frontier in research and in maritime industrial applications, and they usually require a large amount of labelled data, i.e., sensor measurements plus the associated engine status usually annotated by human operators, which are costly and seldomly available in the wild. Unlabelled samples, instead, are commonly, cheaply, and readily available. Hypothesis: The enabling technology for data-driven methods is the availability of a network of sensors and an automation system able to capture and store the associated stream of data. Methods: In this paper, we design and propose multiple alternatives toward the weakly supervised marine dual fuel engines data-driven monitoring. To this aim, we will rely on a Digital Twin of the dual fuel engine or on novelty detection algorithms and we will compare them against state-of-the-art fully supervised approaches. Results: Results on data generated from a real-data validated simulator of a marine dual fuel engine demonstrate that the proposed weakly supervised monitoring approaches lead to a negligible loss in accuracy compared to costly and often unfeasible fully supervised ones supporting the validity of the proposal for its application in the wild. Conclusion: The main outcome is a guideline for selecting the best data-driven dual fuel engine monitoring method according to the available data.