Data Analytics for Ship Performance Assessment Considering Measurement Uncertainties

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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.