Vhi: Valve health identification for the maintenance of subsea industrial equipment

M. Atif Qureshi; Luis Miralles-Pechuán; Jing Su; Jason Payne; Ronan O’Malley

doi:10.1007/978-3-030-10997-4_48

Vhi: Valve health identification for the maintenance of subsea industrial equipment

M. Atif Qureshi, Luis Miralles-Pechuán, Jing Su, Jason Payne, Ronan O’Malley

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

Subsea valves are a key piece of equipment in the extraction process of oil and natural gas. Valves control the flow of fluids by opening and closing passageways. A malfunctioning valve can lead to significant operational losses. In this paper, we describe VHI, a system designed to assist maintenance engineers with condition-based monitoring services for valves. VHI addresses the challenge of maintenance in two ways: a supervised approach that predicts impending valve failure, and an unsupervised approach that identifies and highlights anomalies i.e., an unusual valve behaviour. While the supervised approach is suitable for valves with long operational history, the unsupervised approach is suitable for valves with no operational history.

Original language	English
Title of host publication	Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings
Editors	Ulf Brefeld, Alice Marascu, Fabio Pinelli, Edward Curry, Brian MacNamee, Neil Hurley, Elizabeth Daly, Michele Berlingerio
Publisher	Springer Verlag
Pages	668-671
Number of pages	4
ISBN (Print)	9783030109967
DOIs	https://doi.org/10.1007/978-3-030-10997-4_48
Publication status	Published - 2019
Externally published	Yes
Event	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2018 - Dublin, Ireland Duration: 10 Sep 2018 → 14 Sep 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11053 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2018
Country/Territory	Ireland
City	Dublin
Period	10/09/18 → 14/09/18

Access to Document

10.1007/978-3-030-10997-4_48

Cite this

Atif Qureshi, M., Miralles-Pechuán, L., Su, J., Payne, J., & O’Malley, R. (2019). Vhi: Valve health identification for the maintenance of subsea industrial equipment. In U. Brefeld, A. Marascu, F. Pinelli, E. Curry, B. MacNamee, N. Hurley, E. Daly, & M. Berlingerio (Eds.), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings (pp. 668-671). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11053 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-10997-4_48

Atif Qureshi, M. ; Miralles-Pechuán, Luis ; Su, Jing et al. / Vhi : Valve health identification for the maintenance of subsea industrial equipment. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings. editor / Ulf Brefeld ; Alice Marascu ; Fabio Pinelli ; Edward Curry ; Brian MacNamee ; Neil Hurley ; Elizabeth Daly ; Michele Berlingerio. Springer Verlag, 2019. pp. 668-671 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Vhi: Valve health identification for the maintenance of subsea industrial equipment",

abstract = "Subsea valves are a key piece of equipment in the extraction process of oil and natural gas. Valves control the flow of fluids by opening and closing passageways. A malfunctioning valve can lead to significant operational losses. In this paper, we describe VHI, a system designed to assist maintenance engineers with condition-based monitoring services for valves. VHI addresses the challenge of maintenance in two ways: a supervised approach that predicts impending valve failure, and an unsupervised approach that identifies and highlights anomalies i.e., an unusual valve behaviour. While the supervised approach is suitable for valves with long operational history, the unsupervised approach is suitable for valves with no operational history.",

author = "{Atif Qureshi}, M. and Luis Miralles-Pechu{\'a}n and Jing Su and Jason Payne and Ronan O{\textquoteright}Malley",

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Atif Qureshi, M , Miralles-Pechuán, L, Su, J, Payne, J & O’Malley, R 2019, Vhi: Valve health identification for the maintenance of subsea industrial equipment. in U Brefeld, A Marascu, F Pinelli, E Curry, B MacNamee, N Hurley, E Daly & M Berlingerio (eds), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11053 LNAI, Springer Verlag, pp. 668-671, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2018, Dublin, Ireland, 10/09/18. https://doi.org/10.1007/978-3-030-10997-4_48

Vhi: Valve health identification for the maintenance of subsea industrial equipment. / Atif Qureshi, M.; Miralles-Pechuán, Luis; Su, Jing et al.
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings. ed. / Ulf Brefeld; Alice Marascu; Fabio Pinelli; Edward Curry; Brian MacNamee; Neil Hurley; Elizabeth Daly; Michele Berlingerio. Springer Verlag, 2019. p. 668-671 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11053 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - O’Malley, Ronan

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AB - Subsea valves are a key piece of equipment in the extraction process of oil and natural gas. Valves control the flow of fluids by opening and closing passageways. A malfunctioning valve can lead to significant operational losses. In this paper, we describe VHI, a system designed to assist maintenance engineers with condition-based monitoring services for valves. VHI addresses the challenge of maintenance in two ways: a supervised approach that predicts impending valve failure, and an unsupervised approach that identifies and highlights anomalies i.e., an unusual valve behaviour. While the supervised approach is suitable for valves with long operational history, the unsupervised approach is suitable for valves with no operational history.

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Atif Qureshi M , Miralles-Pechuán L, Su J, Payne J, O’Malley R. Vhi: Valve health identification for the maintenance of subsea industrial equipment. In Brefeld U, Marascu A, Pinelli F, Curry E, MacNamee B, Hurley N, Daly E, Berlingerio M, editors, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings. Springer Verlag. 2019. p. 668-671. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-10997-4_48

Vhi: Valve health identification for the maintenance of subsea industrial equipment

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