Robustness of maintenance decisions: Uncertainty modelling and value of information

Athena Zitrou, Tim Bedford, Alireza Daneshkhah

Research output: Contribution to journalArticle

23 Citations (Scopus)

Abstract

In this paper we show how sensitivity analysis for a maintenance optimisation problem can be undertaken by using the concept of expected value of perfect information (EVPI). This concept is important in a decision-theoretic context such as the maintenance problem, as it allows us to explore the effect of parameter uncertainty on the cost and the resulting recommendations. To reduce the computational effort required for the calculation of EVPIs, we have used Gaussian process (GP) emulators to approximate the cost rate model. Results from the analysis allow us to identify the most important parameters in terms of the benefit of ’learning’ by focussing on the partial expected value of perfect information for a parameter. The analysis determines the optimal solution and the expected related cost when the parameters are unknown and partially known. This type of analysis can be used to ensure that both maintenance calculations and resulting recommendations are sufficiently robust.
LanguageEnglish
Pages60-71
Number of pages12
JournalReliability Engineering and System Safety
Volume120
Early online date13 Mar 2013
DOIs
Publication statusPublished - Dec 2013

Fingerprint

Value of Information
Uncertainty Modeling
Maintenance
Robustness
Expected Value
Recommendations
Costs
Parameter Uncertainty
Gaussian Process
Sensitivity analysis
Sensitivity Analysis
Optimal Solution
Optimization Problem
Partial
Unknown
Uncertainty
Concepts
Model

Keywords

  • maintenance
  • optimisation
  • value of information
  • emulator
  • gaussian process

Cite this

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Robustness of maintenance decisions : Uncertainty modelling and value of information. / Zitrou, Athena; Bedford, Tim; Daneshkhah, Alireza.

In: Reliability Engineering and System Safety, Vol. 120, 12.2013, p. 60-71.

Research output: Contribution to journalArticle

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