Empirical Bayes methodology for estimating equipment failure rates with application to power generation plants

Kenneth Hutchison, John Quigley, M. Raza, L.A. Walls

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)
251 Downloads (Pure)

Abstract

Many reliability databases pool event data for equipment across different plants. Pooling may occur both within and between organizations with the intention of sharing data across common items within similar operating environments to provide better estimates of reliability and availability. Frequentist estimation methods can be poor when few, or no, events occur even when equipment operate for long periods. An alternative approach based upon empirical Bayes estimation is proposed. The new method is applied to failure data analysis in power generation plants and found to provide credible insights. A statistical comparison between the proposed and frequentist methods shows that empirical Bayes is capable of generating more accurate estimates.
Original languageEnglish
Title of host publicationIEEE International Conference on Industrial Engineering and Engineering Management, 2008
PublisherIEEE
Pages1359-1364
Number of pages5
Volume1-3
ISBN (Print)9781424426294
DOIs
Publication statusPublished - 2008

Publication series

NameInternational Conference on Industrial Engineering and Engineering Management IEEM
PublisherIEEE

Keywords

  • Bayes methods
  • data analysis
  • estimation theory
  • failure analysis
  • power apparatus
  • power engineering computing
  • power generation faults
  • power plants

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  • Cite this

    Hutchison, K., Quigley, J., Raza, M., & Walls, L. A. (2008). Empirical Bayes methodology for estimating equipment failure rates with application to power generation plants. In IEEE International Conference on Industrial Engineering and Engineering Management, 2008 (Vol. 1-3, pp. 1359-1364). (International Conference on Industrial Engineering and Engineering Management IEEM). IEEE. https://doi.org/10.1109/IEEM.2008.4738092