A quantile dependency model for predicting optimal centrifugal pump operating strategies

Bruce Stephen, Blair Brown, Andrew Young, Andrew Duncan, Henrique Helfer-Hoeltgebaum, Graeme West, Craig Michie, Stephen D. J. McArthur

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
25 Downloads (Pure)

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.

Original languageEnglish
Article number557
Number of pages17
JournalMachines
Volume10
Issue number7
Early online date10 Jul 2022
DOIs
Publication statusPublished - Jul 2022

Keywords

  • rotating plant
  • vibration monitoring
  • thermal generation
  • probabilistic modelling

Fingerprint

Dive into the research topics of 'A quantile dependency model for predicting optimal centrifugal pump operating strategies'. Together they form a unique fingerprint.

Cite this