Abstract
| Language | English |
|---|---|
| Title of host publication | Advances in Safety, Reliability and Risk Management |
| Editors | Christophe Bérengeur, Antoine Grall, Carlos Guedes Soares |
| Pages | 940-948 |
| Publication status | Published - Aug 2011 |
| Event | ESREL 2011 - 20th European Safety and Reliability Conference - Troyes, France Duration: 18 Sep 2011 → 22 Sep 2011 |
Conference
| Conference | ESREL 2011 - 20th European Safety and Reliability Conference |
|---|---|
| Country | France |
| City | Troyes |
| Period | 18/09/11 → 22/09/11 |
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Keywords
- maintenance decisions
- sensitivity analysis
- Expected Value of Perfect Information
- EVPI
- Gaussian process
Cite this
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Robustness of maintenance decisions : uncertainty modelling and value of information. / Zitrou, Athena; Bedford, Tim; Daneshkhah, Ali.
Advances in Safety, Reliability and Risk Management. ed. / Christophe Bérengeur; Antoine Grall; Carlos Guedes Soares. 2011. p. 940-948.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book
TY - GEN
T1 - Robustness of maintenance decisions
T2 - uncertainty modelling and value of information
AU - Zitrou, Athena
AU - Bedford, Tim
AU - Daneshkhah, Ali
PY - 2011/8
Y1 - 2011/8
N2 - 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. Assuming that a parameter can become completely known before a maintenance decision is made, the analysis determines the optimal decision and the expected related cost, for the different values of the parameter. This type of analysis can be used to ensure that both maintenance calculations and resulting recommendations are sufficiently robust.
AB - 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. Assuming that a parameter can become completely known before a maintenance decision is made, the analysis determines the optimal decision and the expected related cost, for the different values of the parameter. This type of analysis can be used to ensure that both maintenance calculations and resulting recommendations are sufficiently robust.
KW - maintenance decisions
KW - sensitivity analysis
KW - Expected Value of Perfect Information
KW - EVPI
KW - Gaussian process
UR - http://www.esrel2011.com/images/ESREL2011_Programme.pdf
M3 - Conference contribution book
SN - 978-0-415-68379-1
SP - 940
EP - 948
BT - Advances in Safety, Reliability and Risk Management
A2 - Bérengeur, Christophe
A2 - Grall, Antoine
A2 - Soares, Carlos Guedes
ER -