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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.

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2001.06916 - Author's Original
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2001.06916v2 - Author's Original
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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 Anthony J. Dunn: ORCID iD orcid.org/0009-0006-1179-117X
ORCID for Alain B. Zemkoho: ORCID iD orcid.org/0000-0003-1265-4178

Catalogue record

Date deposited: 30 Mar 2021 16:35
Last modified: 17 Mar 2024 03:40

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Contributors

Author: Anthony J. Dunn ORCID iD

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