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A one-class SVM based approach for condition-based maintenance of a naval propulsion plant with limited labeled data

机译：一种基于SVM的方法，用于有限条件标记数据的海军推进装置的状态维护

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摘要

Maintenance of the main propulsion system is an important part of ship operation, whose costs account for the most of the costs of ship machinery maintenance. Related literature has shown that condition-based maintenance can minimize maintenance costs and machinery downtime. In this respect, we propose a one-class support vector machine based approach for machinery decay status assessment which helps to achieve condition-based maintenance. The novelty of our work lies in the using of decision values returned by the trained models to estimate the decay degree and the main decay direction. In particular, only the normal data and a small amount of labeled decayed data is necessary in model training process which greatly reduces the requirement for labeled data. Consequently, it can be used as a decision support tool for condition-based maintenance not only on this plant, but also on many other machinery.
机译：主推进系统的维护是船舶运行的重要组成部分，其成本占船舶机械维护成本的大部分。相关文献表明，基于状态的维护可以最大程度地减少维护成本和机器停机时间。在这方面，我们提出了一种基于一类支持向量机的方法来进行机械衰减状态评估，该方法有助于实现基于状态的维护。我们工作的新颖性在于使用经过训练的模型返回的决策值来估计衰减程度和主要衰减方向。特别地，在模型训练过程中仅需要正常数据和少量标记的衰减数据，这大大减少了对标记数据的需求。因此，它不仅可以用作该工厂以及许多其他机器上基于状态维护的决策支持工具。

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