Ensemble-approaches for clustering health status of oil sand pumps

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

36 Scopus Citations
View graph of relations

Author(s)

  • F. Di Maio
  • J. Hu
  • M. Pecht
  • E. Zio

Detail(s)

Original languageEnglish
Pages (from-to)4847-4859
Journal / PublicationExpert Systems with Applications
Volume39
Issue number5
Online published20 Oct 2011
Publication statusPublished - Apr 2012

Abstract

Centrifugal slurry pumps are widely used in the oil sand industry, mining, ore processing, waste treatment, cement production, and other industries to move mixtures of solids and liquids. Wear of slurry pump components, caused by abrasive and erosive solid particles, is one of the main causes of reduction in the efficiency and useful life of these pumps. This leads to unscheduled outages that cost companies millions of dollars each year. Traditional maintenance strategies can be applied, but they provide insufficient warning of impending failures. On the other hand, condition monitoring and on-line assessment of the wear status of wetted components in slurry pumps are expected to improve maintenance management and generate significant cost savings for pump operators. In this context, the objective of the present work is to develop and compare two unsupervised clustering ensemble methods, i.e.; fuzzy C-means and hierarchical trees, for the assessment and measurement of the wear status of slurry pumps when available data is extremely limited. The idea is to combine predictions of multiple classifiers to reduce the variance of the results so that they are less dependent on the specifics of a single classifier. This will also reduce the variance of the bias, because a combination of multiple classifiers may learn a more expressive concept class than a single classifier.

Research Area(s)

  • Degradation, Ensembles of classifiers, Fault detection, Fuzzy C-means, Hierarchical tree, Slurry pumps

Citation Format(s)

Ensemble-approaches for clustering health status of oil sand pumps. / Di Maio, F.; Hu, J.; Tse, P.; Pecht, M.; Tsui, K.; Zio, E.

In: Expert Systems with Applications, Vol. 39, No. 5, 04.2012, p. 4847-4859.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal