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.

中文翻译：

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通过弱监督数据分析在野外监控船用双燃料发动机

背景：

海上运输约占全球货运量的80％，对全球环境足迹做出了显着贡献。既可以使用气态燃料也可以使用液态燃料的双燃料发动机，是减少排放量的可行方法，但同时又增加了监控活动的复杂性。

动机：

数据驱动的方法代表了研究和海洋工业应用中的前沿领域，它们通常需要大量的标记数据，即传感器测量值以及通常由操作员注释的相关发动机状态，这在野外是昂贵且很少获得的。相反，未标记的样品通常，便宜且容易获得。

假设：

数据驱动方法的使能技术是传感器网络和能够捕获和存储相关数据流的自动化系统的可用性。

方法：

在本文中，我们针对弱监督船用双燃料发动机数据驱动的监测设计并提出了多种替代方案。为此，我们将依靠双燃料发动机的Digital Twin或新颖性检测算法，并将它们与最先进的完全监督方法进行比较。

结果：

由船用双燃料发动机的经过实际数据验证的模拟器生成的数据结果表明，与成本高昂且通常不可行的全面监督方法相比，所提议的弱监督监视方法所导致的准确性损失可忽略不计，这证明了该建议在其应用中的有效性在野外。

结论：

主要结果是根据可用数据选择最佳数据驱动的双燃料发动机监控方法的指南。