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

Andrea Coraddu, Luca Oneto, Francesco Baldi, Francesca Cipollini, Mehmet Atlar, Stefano Savio

Research output: Contribution to journalArticle

5 Citations (Scopus)
2 Downloads (Pure)

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.

Original languageEnglish
Article number106063
Number of pages14
JournalOcean Engineering
Volume186
Early online date13 Jun 2019
DOIs
Publication statusPublished - 15 Aug 2019

Keywords

  • condition based maintenance
  • data-driven Models
  • deep learning
  • digital twin
  • fouling
  • hull and propeller maintenance
  • ISO 19030

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