TY - JOUR
T1 - Data-driven ship digital twin for estimating the speed loss caused by the marine fouling
AU - Coraddu, Andrea
AU - Oneto, Luca
AU - Baldi, Francesco
AU - Cipollini, Francesca
AU - Atlar, Mehmet
AU - Savio, Stefano
PY - 2019/8/15
Y1 - 2019/8/15
N2 - 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.
AB - 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.
KW - condition based maintenance
KW - data-driven Models
KW - deep learning
KW - digital twin
KW - fouling
KW - hull and propeller maintenance
KW - ISO 19030
UR - http://www.scopus.com/inward/record.url?scp=85067817812&partnerID=8YFLogxK
U2 - 10.1016/j.oceaneng.2019.05.045
DO - 10.1016/j.oceaneng.2019.05.045
M3 - Article
AN - SCOPUS:85067817812
SN - 0029-8018
VL - 186
JO - Ocean Engineering
JF - Ocean Engineering
M1 - 106063
ER -