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A novelty detection approach to diagnosing hull and propeller fouling

A novelty detection approach to diagnosing hull and propeller fouling
A novelty detection approach to diagnosing hull and propeller fouling

Hull and propeller performance have a primary role in overall vessel efficiency. Vessel fouling is a common phenomenon where undesirable substances attach or grow on the ship hull. A clear understanding of the extent of the degradation of the hull will allow better management of assets and prediction of the best time for dry docking and hull maintenance work. In this paper, the authors investigate the problems of predicting the hull condition in real operations based on data measured by the on-board systems. The proposed solution uses an unsupervised Machine Learning (ML) modelling technique to eliminate the need for collecting labeled data related to the hull and propeller fouling condition. Two anomaly detection methods based on Support Vector Machines and k-nearest neighbour have been applied to predict the hull condition using the available parameters measured on-board. Data from the Research Vessel The Princess Royal has been exploited to show the effectiveness of the proposed methods and to benchmark them in a realistic maritime application.

Data analytics, Hull and propeller performance, Hull fouling detection, Sensor data collection, Ship efficiency, Supervised learning
0029-8018
65-73
Coraddu, Andrea
eb41a72b-88f2-43f2-b685-ed948f2aa818
Lim, Serena
a63d31ff-97a7-4de6-88de-21b6e046ed49
Oneto, Luca
ef12572a-bad4-4fe3-8451-2cf82a5d3ac5
Pazouki, Kayvan
1e69a646-83da-49ce-af3a-c40808c83ffe
Norman, Rose
6d2518aa-ece8-498f-82dc-dee5ec7b1b37
Murphy, Alan John
8e021dad-0c60-446b-a14e-cddd09d44626
Coraddu, Andrea
eb41a72b-88f2-43f2-b685-ed948f2aa818
Lim, Serena
a63d31ff-97a7-4de6-88de-21b6e046ed49
Oneto, Luca
ef12572a-bad4-4fe3-8451-2cf82a5d3ac5
Pazouki, Kayvan
1e69a646-83da-49ce-af3a-c40808c83ffe
Norman, Rose
6d2518aa-ece8-498f-82dc-dee5ec7b1b37
Murphy, Alan John
8e021dad-0c60-446b-a14e-cddd09d44626

Coraddu, Andrea, Lim, Serena, Oneto, Luca, Pazouki, Kayvan, Norman, Rose and Murphy, Alan John (2019) A novelty detection approach to diagnosing hull and propeller fouling. Ocean Engineering, 176, 65-73. (doi:10.1016/j.oceaneng.2019.01.054).

Record type: Article

Abstract

Hull and propeller performance have a primary role in overall vessel efficiency. Vessel fouling is a common phenomenon where undesirable substances attach or grow on the ship hull. A clear understanding of the extent of the degradation of the hull will allow better management of assets and prediction of the best time for dry docking and hull maintenance work. In this paper, the authors investigate the problems of predicting the hull condition in real operations based on data measured by the on-board systems. The proposed solution uses an unsupervised Machine Learning (ML) modelling technique to eliminate the need for collecting labeled data related to the hull and propeller fouling condition. Two anomaly detection methods based on Support Vector Machines and k-nearest neighbour have been applied to predict the hull condition using the available parameters measured on-board. Data from the Research Vessel The Princess Royal has been exploited to show the effectiveness of the proposed methods and to benchmark them in a realistic maritime application.

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More information

Accepted/In Press date: 25 January 2019
e-pub ahead of print date: 22 February 2019
Published date: 22 February 2019
Keywords: Data analytics, Hull and propeller performance, Hull fouling detection, Sensor data collection, Ship efficiency, Supervised learning

Identifiers

Local EPrints ID: 483832
URI: http://eprints.soton.ac.uk/id/eprint/483832
ISSN: 0029-8018
PURE UUID: 786cbb8d-ccd5-445f-a06c-235d3b85eb45

Catalogue record

Date deposited: 06 Nov 2023 18:19
Last modified: 10 May 2024 17:03

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Contributors

Author: Andrea Coraddu
Author: Serena Lim
Author: Luca Oneto
Author: Kayvan Pazouki
Author: Rose Norman
Author: Alan John Murphy

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