ABSTRACT

Machine learning is an effective way to realise the condition monitoring of marine machinery. However, it is challenging to realise this purpose based on supervised learning in practice due to the lack of labelled data. To overcome this problem, we propose to use isolation forest to realise the decay detection of a marine gas turbine with normal data. Besides, we consider the impact of data contamination for the first time compared with previous literatures. We also experiment with the same datasets with support vector data description (SVDD) as a comparison. The results show that the isolation forest is very suitable for the decay detection of the marine gas turbine, and it shows a significant advantage over support vector data description in the tolerance to contaminated data. The dataset we experiment with is from a real-data validated numerical simulator developed for a Frigate’s propulsion plant.

摘要

机器学习是实现船舶机械状态监测的有效途径。但是，由于缺乏标记数据，在实践中基于监督学习来实现此目的具有挑战性。为了克服这个问题，我们建议使用隔离森林来实现具有正常数据的船用燃气轮机的衰减检测。此外，与以前的文献相比，我们首次考虑了数据污染的影响。我们还以支持向量数据描述（SVDD）为实验，对相同的数据集进行了比较。结果表明，隔离林非常适用于船用燃气轮机的衰减检测，并且在支持污染数据方面，它在支持向量数据描述方面显示出显着优势。