Data-driven ship digital twin for estimating the speed loss caused by the marine fouling

OCEAN ENGINEERING

卷:186

Article

Coraddu, Andrea¹  Oneto, Luca²  Baldi, Francesco³  Cipollini, Francesca²  Atlar, Mehmet¹  Savio, Stefano⁴ 

[1] Strathclyde Univ, Naval Architecture Ocean & Marine Engn, Glasgow G1 1XW, Lanark, Scotland

[2] Univ Genoa, DIBRIS, Via Opera Pia 13, I-16145 Genoa, Italy

[3] ENEA Italian Natl Agcy New Technol, Energy & Sustainable Econ Dev, Via Martiri Montesole 4, I-40126 Bologna, Italy

[4] Univ Genoa, DITEN, Via Opera Pia 11a, I-16145 Genoa, Italy

关键词: Hull and propeller maintenance;    Fouling;    Condition based maintenance;    ISO 19030;    Digital twin;    Data-Driven Models;    Deep learning;   

DOI  :  10.1016/j.oceaneng.2019.05.045

来源: Elsevier

【 摘 要 】

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.

【 授权许可】

Free