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Condition-based maintenance of naval propulsion systems with supervised data analysis

Condition-based maintenance of naval propulsion systems with supervised data analysis
Condition-based maintenance of naval propulsion systems with supervised data analysis

The behavior and interaction of the main components of Ship Propulsion Systems cannot be easily modeled with a priori physical knowledge, considering the large amount of variables influencing them. Data-Driven Models (DDMs), instead, exploit advanced statistical techniques to build models directly on the large amount of historical data collected by on-board automation systems, without requiring any a priori knowledge. DDMs are extremely useful when it comes to continuously monitoring the propulsion equipment and take decisions based on the actual condition of the propulsion plant. In this paper, the authors investigate the problem of performing Condition-Based Maintenance through the use of DDMs. In order to conceive this purpose, several state-of-the-art supervised learning techniques are adopted, which require labeled sensor data in order to be deployed. A naval vessel, characterized by a combined diesel-electric and gas propulsion plant, has been exploited to collect such data and show the effectiveness of the proposed approaches. Because of confidentiality constraints with the Navy the authors used a real-data validated simulator and the dataset has been published for free use through the UCI repository.

Condition-Based Maintenance, Data Analysis, Naval Propulsion Systems, Supervised learning
0029-8018
268-278
Cipollini, Francesca
e0f39735-d273-4a95-bf22-ec2672896a09
Oneto, Luca
ef12572a-bad4-4fe3-8451-2cf82a5d3ac5
Coraddu, Andrea
eb41a72b-88f2-43f2-b685-ed948f2aa818
Murphy, Alan John
8e021dad-0c60-446b-a14e-cddd09d44626
Anguita, Davide
12909632-e4d6-4e2a-9ac4-483971edc8ca
Cipollini, Francesca
e0f39735-d273-4a95-bf22-ec2672896a09
Oneto, Luca
ef12572a-bad4-4fe3-8451-2cf82a5d3ac5
Coraddu, Andrea
eb41a72b-88f2-43f2-b685-ed948f2aa818
Murphy, Alan John
8e021dad-0c60-446b-a14e-cddd09d44626
Anguita, Davide
12909632-e4d6-4e2a-9ac4-483971edc8ca

Cipollini, Francesca, Oneto, Luca, Coraddu, Andrea, Murphy, Alan John and Anguita, Davide (2017) Condition-based maintenance of naval propulsion systems with supervised data analysis. Ocean Engineering, 149, 268-278. (doi:10.1016/j.oceaneng.2017.12.002).

Record type: Review

Abstract

The behavior and interaction of the main components of Ship Propulsion Systems cannot be easily modeled with a priori physical knowledge, considering the large amount of variables influencing them. Data-Driven Models (DDMs), instead, exploit advanced statistical techniques to build models directly on the large amount of historical data collected by on-board automation systems, without requiring any a priori knowledge. DDMs are extremely useful when it comes to continuously monitoring the propulsion equipment and take decisions based on the actual condition of the propulsion plant. In this paper, the authors investigate the problem of performing Condition-Based Maintenance through the use of DDMs. In order to conceive this purpose, several state-of-the-art supervised learning techniques are adopted, which require labeled sensor data in order to be deployed. A naval vessel, characterized by a combined diesel-electric and gas propulsion plant, has been exploited to collect such data and show the effectiveness of the proposed approaches. Because of confidentiality constraints with the Navy the authors used a real-data validated simulator and the dataset has been published for free use through the UCI repository.

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More information

Accepted/In Press date: 3 December 2017
e-pub ahead of print date: 27 December 2017
Published date: 27 December 2017
Keywords: Condition-Based Maintenance, Data Analysis, Naval Propulsion Systems, Supervised learning

Identifiers

Local EPrints ID: 483835
URI: http://eprints.soton.ac.uk/id/eprint/483835
ISSN: 0029-8018
PURE UUID: 332f8758-f0cc-4338-8b0e-718f24fee96a

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Date deposited: 06 Nov 2023 18:20
Last modified: 10 May 2024 17:03

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Contributors

Author: Francesca Cipollini
Author: Luca Oneto
Author: Andrea Coraddu
Author: Alan John Murphy
Author: Davide Anguita

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