Picture of a black hole

Strathclyde Open Access research that creates ripples...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of research papers by University of Strathclyde researchers, including by Strathclyde physicists involved in observing gravitational waves and black hole mergers as part of the Laser Interferometer Gravitational-Wave Observatory (LIGO) - but also other internationally significant research from the Department of Physics. Discover why Strathclyde's physics research is making ripples...

Strathprints also exposes world leading research from the Faculties of Science, Engineering, Humanities & Social Sciences, and from the Strathclyde Business School.

Discover more...

Robustness of maintenance decisions : uncertainty modelling and value of information

Zitrou, Athena and Bedford, Tim and Daneshkhah, Ali (2011) Robustness of maintenance decisions : uncertainty modelling and value of information. In: Advances in Safety, Reliability and Risk Management. Balkema, pp. 940-948. ISBN 978-0-415-68379-1

Full text not available in this repository. (Request a copy from the Strathclyde author)

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. 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.