The University of Southampton
University of Southampton Institutional Repository
Warning ePrints Soton is experiencing an issue with some file downloads not being available. We are working hard to fix this. Please bear with us.

Infrequent adverse event prediction in low carbon energy production using machine learning

Infrequent adverse event prediction in low carbon energy production using machine learning
Infrequent adverse event prediction in low carbon energy production using machine learning
We address the problem of predicting the occurrence of infrequent adverse events in the context of predictive maintenance. We cast the corresponding machine learning task as an imbalanced classification problem and propose a framework for solving it that is capable of leveraging different classifiers in order to predict the occurrence of an adverse event before it takes place. In particular, we focus on two applications arising in low-carbon energy production: foam formation in anaerobic digestion and condenser tube leakage in the steam turbines of a nuclear power station. The results of an extensive set of omputational experiments show the effectiveness of the techniques that we propose.
cs.LG, math.OC
2331-8422
Coniglio, Stefano
03838248-2ce4-4dbc-a6f4-e010d6fdac67
Dunn, Anthony J.
161d9c8e-6813-4909-95ea-6c11bbbca287
Zemkoho, Alain B.
30c79e30-9879-48bd-8d0b-e2fbbc01269e
Coniglio, Stefano
03838248-2ce4-4dbc-a6f4-e010d6fdac67
Dunn, Anthony J.
161d9c8e-6813-4909-95ea-6c11bbbca287
Zemkoho, Alain B.
30c79e30-9879-48bd-8d0b-e2fbbc01269e

Coniglio, Stefano, Dunn, Anthony J. and Zemkoho, Alain B. (2020) Infrequent adverse event prediction in low carbon energy production using machine learning. arXiv. (In Press)

Record type: Article

Abstract

We address the problem of predicting the occurrence of infrequent adverse events in the context of predictive maintenance. We cast the corresponding machine learning task as an imbalanced classification problem and propose a framework for solving it that is capable of leveraging different classifiers in order to predict the occurrence of an adverse event before it takes place. In particular, we focus on two applications arising in low-carbon energy production: foam formation in anaerobic digestion and condenser tube leakage in the steam turbines of a nuclear power station. The results of an extensive set of omputational experiments show the effectiveness of the techniques that we propose.

Text
2001.06916 - Author's Original
Restricted to Repository staff only
Request a copy
Text
2001.06916v2 - Accepted Manuscript
Download (1MB)

More information

Accepted/In Press date: 27 January 2020
Keywords: cs.LG, math.OC

Identifiers

Local EPrints ID: 448030
URI: http://eprints.soton.ac.uk/id/eprint/448030
ISSN: 2331-8422
PURE UUID: ed62e85f-b7db-4d7f-beef-3fb63e65af61
ORCID for Stefano Coniglio: ORCID iD orcid.org/0000-0001-9568-4385
ORCID for Alain B. Zemkoho: ORCID iD orcid.org/0000-0003-1265-4178

Catalogue record

Date deposited: 30 Mar 2021 16:35
Last modified: 28 Apr 2021 01:46

Export record

Contributors

Author: Anthony J. Dunn

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×