Prognostic modelling of valve degradation within power stations

Mark James McGhee, Grant Galloway, Victoria Catterson, Blair Brown, Emma Harrison

Research output: Contribution to conferencePaperpeer-review

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Within the field of power generation, aging assets and a desire for improved maintenance decision-making tools have led to growing interest in asset prognostics. Valve failures can account for 7% or more of mechanical failures, and since a conventional power station will contain many hundreds of valves, this represents a significant asset base. This paper presents a prognostic approach for estimating the remaining useful life (RUL) of valves experiencing degradation, utilizing a similarity-based method. Case study data is generated through simulation of valves within a 400MW Combined Cycle Gas Turbine power station.

High fidelity industrial simulators are often produced for operator training, to allow personnel to experience fault procedures and take corrective action in safe, simulation environment, without endangering staff or equipment. This work repurposes such a high fidelity simulator to generate the type of condition monitoring data which would be produced in the presence of a fault. A first principles model of valve degradation was used to generate multiple run-to- failure events, at different degradation rates. The associated parameter data was collected to generate a library of failure cases. This set of cases was partitioned into training and test sets for prognostic modelling and the similarity based prognostic technique applied to calculate RUL. Results are presented of the technique’s accuracy, and conclusions are drawn about the applicability of the technique to this domain.
Original languageEnglish
Number of pages6
Publication statusPublished - Sept 2014
EventAnnual Conference of the Prognostics and Health Management Society 2014 (PHM) - Hilton Fort Worth, Fort Worth, United States
Duration: 29 Sept 20142 Oct 2014


ConferenceAnnual Conference of the Prognostics and Health Management Society 2014 (PHM)
Country/TerritoryUnited States
CityFort Worth


  • power generation
  • decision support systems
  • remaining useful life (RUL)
  • prognostic modelling
  • valve degradation


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