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，国际航运将没有温室气体（GHG）排放，但需要考虑各国的具体情况。如果船舶性能监测系统的设计基础是可靠和准确的，那么价格合理的船舶性能监测系统可以为改进的关键领域提供洞察。然而，由于天气条件、生物污垢和传感器公差等因素，存在许多不确定性。本研究调查了数字化和物联网（IoT）在海洋工程中的应用，并将通过使用机器学习技术评估改造后的可持续推进系统的性能。数据收集的关键方法包括在船上安装鲁棒传感器，该传感器可以连续监测环境条件和船舶响应，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着增加，并建议进行进一步研究，以更深入地了解航运部门的数字化及其对国际海事组织零排放目标的贡献。该研究将通过实验评估改造后的可持续推进系统，并使用大数据分析技术和物联网技术来识别和解决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.