Data Analytics for Ship Performance Assessment Considering Measurement Uncertainties

国际海事组织(IMO)修订后的战略规定，到2050年，国际航运将实现温室气体(GHG)零排放，但警告称，将考虑个别国家的情况。如果设计的基础是可靠和准确的，负担得起的船舶性能监测系统可以提供对关键改进领域的洞察。然而，由于天气条件、生物污垢和传感器耐受性等因素，存在许多不确定性。这项研究调查了数字化和物联网(IoT)在海洋工程中的应用，并将使用机器学习技术评估改装后的可持续推进系统的性能。数据采集的关键方法是在船上安装坚固的传感器，它可以连续监测环境条件和船舶的响应，并结合算法分析数据并实时提供船舶性能。文献综述表明，关于这些主题的已发表论文显著增加，并建议进行进一步研究，以更深入地了解航运部门数字化及其对国际海事组织零排放目标的贡献。这项研究将通过实验评估改装的可持续推进系统，并使用大数据分析技术和物联网技术来识别和处理性能数据中的不确定性。

The International Maritime Organization's (IMO) revised strategy is imposing that international shipping be free of greenhouse gas (GHG) emissions by 2050 with the caveat that individual national circumstances will be considered. Affordable ship performance monitoring systems could provide insight into the key areas of improvement if the foundation of their design is reliable and accurate. However, there are a number of uncertainties originating from factors such as weather conditions, biofouling, and sensor tolerance. This study investigates the application of digitalisation and the Internet of Things (IoT) in Marine Engineering and will assess the performance of a retrofitted sustainable propulsion system by using machine learning technology. The key method for data collection consists of the installation of robust sensors onboard the ship which could continuously monitor the environmental conditions and the ship's response combined with algorithms to analyse data and provide ship performance in real-time. The literature review indicates a significant increase in published papers on these topics having the recommendations of further research to be conducted for a deeper understanding of the shipping sector digitalization and its contribution towards IMO's goal of zero emissions.This research will assess a retrofitted sustainable propulsion system experimentally and use big data analysis techniques and IoT technology to identify and address uncertainty in the performance data.