Prognostic Modeling of Valve Degradation within Power Stations

Abstract

Within the field of power generation, aging assets and a desire for improved maintenance decision-making tool shave 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 a400MW 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 a safe, simulation environment, without endangering staff or equipment. This worker purposes 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 modeling and the similarity based prognostic technique applied to calculate RUL. Results are presented of the techniques accuracy, and conclusions are drawn about the applicability of the technique to this domain.

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Document Details

Document Type
Technical Report
Publication Date
Oct 02, 2014
Accession Number
AD1002284

Entities

People

  • Bonnie R. Brown
  • E. Harrison
  • G. Galloway
  • M. J. Mcghee
  • V. M. Catterson

Organizations

  • University of Strathclyde

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Case Studies
  • Degradation
  • Demographic Cohorts
  • Electrical Engineering
  • Engineering
  • Environment
  • Failure Mode And Effect Analysis
  • Gas Turbines
  • Maintenance
  • Monitoring
  • Reliability
  • Simulations
  • Simulators
  • Students
  • Training
  • Turbines

Fields of Study

  • Engineering

Readers

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