A quantile dependency model for predicting optimal centrifugal pump operating strategies
Stephen, Bruce and Brown, Blair and Young, Andrew and Duncan, Andrew and Helfer-Hoeltgebaum, Henrique and West, Graeme and Michie, Craig and McArthur, Stephen D. J. (2022) A quantile dependency model for predicting optimal centrifugal pump operating strategies. Machines, 10 (7). 557. ISSN 2075-1702 (https://doi.org/10.3390/machines10070557)
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Abstract
Used in many industrial applications, centrifugal pumps have optimal operating criteria specified at design. These criteria may not be precisely adhered to during operation which will ultimately reduce the life of the asset. Operators would therefore benefit from anticipating how often the design point is deviated from and hence how much asset degradation results. For centrifugal pumps, a novel set of covariates were proposed in this paper which formally partition observed operating zones with an Empirical Bivariate Quantile Partitioned distribution. This captured the dependency relation between operating parameters across plant configurations to predict the component wear that results from particular settings. The effectiveness of this was demonstrated through an operational case study in civil nuclear generation feedwater pumps where corroboration with bearing movements provides an indicator of plant wear. Such a technique is envisaged to inform operators of optimal plant configuration from multiple possibilities in advance of undertaking them.
ORCID iDs
Stephen, Bruce ORCID: https://orcid.org/0000-0001-7502-8129, Brown, Blair, Young, Andrew ORCID: https://orcid.org/0000-0001-6338-6631, Duncan, Andrew, Helfer-Hoeltgebaum, Henrique, West, Graeme ORCID: https://orcid.org/0000-0003-0884-6070, Michie, Craig ORCID: https://orcid.org/0000-0001-5132-4572 and McArthur, Stephen D. J. ORCID: https://orcid.org/0000-0003-1312-8874;-
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Item type: Article ID code: 81428 Dates: DateEventJuly 2022Published10 July 2022Published Online3 July 2022AcceptedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering
Strategic Research Themes > Energy
Strategic Research Themes > Measurement Science and Enabling Technologies
Faculty of EngineeringDepositing user: Pure Administrator Date deposited: 11 Jul 2022 15:02 Last modified: 12 Dec 2024 13:26 URI: https://strathprints.strath.ac.uk/id/eprint/81428