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Condition-based maintenance of naval propulsion systems: Data analysis with minimal feedback

机译：基于状态的海军推进系统维护：反馈最少的数据分析

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摘要

The maintenance of the several components of a Ship Propulsion Systems is an onerous activity, which need to be efficiently programmed by a shipbuilding company in order to save time and money. The replacement policies of these components can be planned in a Condition-Based fashion, by predicting their decay state and thus proceed to substitution only when really needed. In this paper, authors propose several Data Analysis supervised and unsupervised techniques for the Condition-Based Maintenance of a vessel, characterised by a combined diesel-electric and gas propulsion plant. In particular, this analysis considers a scenario where the collection of vast amounts of labelled data containing the decay state of the components is unfeasible. In fact, the collection of labelled data requires a drydocking of the ship and the intervention of expert operators, which is usually an infrequent event. As a result, authors focus on methods which could allow only a minimal feedback from naval specialists, thus simplifying the dataset collection phase. Confidentiality constraints with the Navy require authors to use a real-data validated simulator and the dataset has been published for free use through the OpenML repository.
机译：船舶推进系统几个部件的维护是一项繁重的工作，造船公司需要对其进行有效编程，以节省时间和金钱。这些组件的替换策略可以通过基于条件的方式来计划，方法是预测它们的衰减状态，从而仅在真正需要时才进行替换。在本文中，作者提出了几种以状态分析为基础的船舶状态检修的数据分析监督技术和无监督技术，这些技术以柴油-电力和气体推进装置相结合为特征。特别是，该分析考虑了以下情况：收集包含组件衰减状态的大量标记数据是不可行的。实际上，标记数据的收集需要对船舶进行干坞停放和专家操作员的干预，这通常是很少发生的事件。结果，作者专注于只能允许海军专家提供最小反馈的方法，从而简化了数据集的收集阶段。海军的机密性要求作者使用经过实际数据验证的模拟器，并且该数据集已经发布，可以通过OpenML存储库免费使用。

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