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Condition-based maintenance of naval propulsion systems: data analysis with minimal feedback

Condition-based maintenance of naval propulsion systems: data analysis with minimal feedback
Condition-based maintenance of naval propulsion systems: data analysis with minimal feedback

The maintenance of the several components of a Ship Propulsion Systems is an onerous activity, which need to be efficiently programmed by a shipbuilding company in order to save time and money. The replacement policies of these components can be planned in a Condition-Based fashion, by predicting their decay state and thus proceed to substitution only when really needed. In this paper, authors propose several Data Analysis supervised and unsupervised techniques for the Condition-Based Maintenance of a vessel, characterised by a combined diesel-electric and gas propulsion plant. In particular, this analysis considers a scenario where the collection of vast amounts of labelled data containing the decay state of the components is unfeasible. In fact, the collection of labelled data requires a drydocking of the ship and the intervention of expert operators, which is usually an infrequent event. As a result, authors focus on methods which could allow only a minimal feedback from naval specialists, thus simplifying the dataset collection phase. Confidentiality constraints with the Navy require authors to use a real-data validated simulator and the dataset has been published for free use through the OpenML repository.

Condition-based maintenance, Data analysis, Minimal feedback., Naval propulsion systems, Novelty detection, Supervised learning, Unsupervised learning
0951-8320
12-23
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 (2018) Condition-based maintenance of naval propulsion systems: data analysis with minimal feedback. Reliability Engineering and System Safety, 177, 12-23. (doi:10.1016/j.ress.2018.04.015).

Record type: Article

Abstract

The maintenance of the several components of a Ship Propulsion Systems is an onerous activity, which need to be efficiently programmed by a shipbuilding company in order to save time and money. The replacement policies of these components can be planned in a Condition-Based fashion, by predicting their decay state and thus proceed to substitution only when really needed. In this paper, authors propose several Data Analysis supervised and unsupervised techniques for the Condition-Based Maintenance of a vessel, characterised by a combined diesel-electric and gas propulsion plant. In particular, this analysis considers a scenario where the collection of vast amounts of labelled data containing the decay state of the components is unfeasible. In fact, the collection of labelled data requires a drydocking of the ship and the intervention of expert operators, which is usually an infrequent event. As a result, authors focus on methods which could allow only a minimal feedback from naval specialists, thus simplifying the dataset collection phase. Confidentiality constraints with the Navy require authors to use a real-data validated simulator and the dataset has been published for free use through the OpenML repository.

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More information

Accepted/In Press date: 18 April 2018
e-pub ahead of print date: 19 April 2018
Published date: 3 May 2018
Keywords: Condition-based maintenance, Data analysis, Minimal feedback., Naval propulsion systems, Novelty detection, Supervised learning, Unsupervised learning

Identifiers

Local EPrints ID: 483837
URI: http://eprints.soton.ac.uk/id/eprint/483837
ISSN: 0951-8320
PURE UUID: 9c5e472e-5f5f-4f9f-b8fc-a93a2bdf1402

Catalogue record

Date deposited: 06 Nov 2023 18:22
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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