中国舰船研究2025，Vol.20Issue(z1)：88-95,8.DOI:10.19693/j.issn.1673-3185.04395
营运数据驱动的船舶污底评估方法研究
Operational data-driven hull fouling assessment method for ships
房新楠 1秦尧 1伍冬阳 2戴志琳 2李永念2
作者信息
- 1. 上海船舶研究设计院,上海 201203||上海交通大学 自动化与感知学院,上海 200240
- 2. 上海船舶研究设计院,上海 201203
- 折叠
摘要
Abstract
[Objective]Hull fouling severely impairs the sailing efficiency of ships.In actual ship opera-tions,fouling removal is usually conducted within the scheduled dry-docking period.However,this approach often fails to perform fouling removal in a timely manner at the optimal time,resulting in a substantial in-crease in ship fuel consumption costs.To address this issue,this study proposes an operational data-driven hull fouling assessment method.This method can real-time evaluate the performance loss caused by hull fouling,thereby providing a basis for fouling removal decision-making.[Method]First,based on the data collected from the ship during the non-fouling period and the meteorological forecast data,a multi-layer neural network model is established.This model is designed to achieve accurate prediction of fuel consumption per nautical mile.Then,the hull fouling condition is assessed by comparing the deviation between the prediction results of the non-fouling model and the actual measured values.Additionally,a distance threshold screening method is adopted to filter the data in the assessment segment.This step aims to avoid prediction errors caused by model drift and ensure the reliability of the assessment results.[Results]Three segments of non-training data were selected for validation,corresponding to the periods before hull fouling,during hull fouling,and after fouling removal,respectively.For the data of the pre-fouling and post-fouling-removal periods,the percentage devia-tion of the model's predicted fuel consumption per nautical mile was approximately 7%.In contrast,for the da-ta of the in-fouling period,the percentage deviation of the model's predicted fuel consumption per nautical mile exceeded 13%,showing a significant increase in model deviation during the hull fouling period.[Conclusions]The validation results demonstrate that the proposed method can effectively assess the hull fouling condition of ships.The percentage deviation between the model's predicted values and the actual mea-sured values can be regarded as the incremental fuel consumption caused by hull fouling.This finding is con-ducive to further calculating the benefits of fouling removal.关键词
智能船舶/污底评估/神经网络/能效优化Key words
intelligent ships/hull fouling assessment/neural network/energy efficiency optimization分类
交通工程引用本文复制引用
房新楠,秦尧,伍冬阳,戴志琳,李永念..营运数据驱动的船舶污底评估方法研究[J].中国舰船研究,2025,20(z1):88-95,8.
