Data-driven ship digital twin for estimating the speed loss caused by the marine fouling. (15th August 2019)

• Record Type: Journal Article
• Authors: Coraddu, Andrea; Oneto, Luca; Baldi, Francesco; Cipollini, Francesca; Atlar, Mehmet; Savio, Stefano
• Is Part Of: Ocean engineering. Volume 186(2019)
• Publication Date: 2019-08-15
• Subjects: Hull and propeller maintenance -- Fouling -- Condition based maintenance -- ISO 19030 -- Digital twin -- Data-Driven Models -- Deep learning; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2019.05.045
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: Shipping is responsible for approximately the 90% of world trade leading to significant impacts on the environment. As a consequence, a crucial issue for the maritime industry is to develop technologies able to increase the ship efficiency, by reducing fuel consumption and unnecessary maintenance operations. For example, the marine fouling phenomenon has a deep impact, since to prevent or reduce its growth which affects the ship consumption, costly drydockings for cleaning the hull and the propeller are needed and must be scheduled based on a speed loss estimation. In this work a data driven Digital Twin of the ship is built, leveraging on the large amount of information collected from the on-board sensors, and is used for estimating the speed loss due to marine fouling. A thorough comparison between the proposed method and ISO 19030, which is the de-facto standard for dealing with this task, is carried out on real-world data coming from two Handymax chemical/product tankers. Results clearly show the effectiveness of the proposal and its better speedloss prediction accuracy with respect to the ISO 19030, thus allowing reducing the fuel consumption due to fouling. Highlights: A real-data validated numerical model for the reduction of a ship fuel consumption is presented. The approach was compared with the state-of-the-art procedure for predicting the speed loss. Deep Learning models are adopted to investigate the problem of predicting a rise in the speed loss due to Fouling. Results on real data confirm the possibility to treat this problem adopting Data Driven fashion. Deep Learning models obtained higher accuracies with respect to the ISO 19030.