Condition-based maintenance of naval propulsion systems

Data analysis with minimal feedback

Journal article (2018)

Authors

Francesca Cipollini University of Genova

Luca Oneto University of Genova

A. Coraddu University of Strathclyde

Alan John Murphy Newcastle University

Davide Anguita University of Genova

Affiliation

External organisation

Data analysis Unsupervised learning Condition-based maintenance Supervised learning Novelty detection Minimal feedback. Naval propulsion systems

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Published Date

2018-9

Language

English

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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.