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An optimal maintenance policy based on partial information

Ahmadi, Reza and Wu, Shaomin (2018) An optimal maintenance policy based on partial information. In: Haugen, Stein and Barros, Anne and van Gulijk, Coen and Kongsvik, Trond and Vinnem, Jan Erik, eds. Safety and Reliability – Safe Societies in a Changing World: Proceedings of ESREL 2018. CRC Press, London, UK, pp. 511-518. ISBN 978-0-8153-8682-7. E-ISBN 978-1-351-17466-4. (doi:10.1201/9781351174664-63) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:67431)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1201/9781351174664-63

Abstract

This paper proposes an integrated model for maintenance scheduling of parallel systems whose failures are detected by inspections. A common characteristic of such systems is that the system failures are detected only by inspections and the failure of a component may not cause its system to fail. As such, the failure may not be immediately detected and the random (disruption) time at which the number of failed components reaches a certain predefined number d may therefore be unknown. For such systems, scheduling maintenance policy is a difficult task, which is tackled in this paper. The main issue considered here is to get an estimate of the disruption time on the basis of inspection point process observations in the framework of filtering theorem. The paper develops a unified cost structure to jointly optimise inspection frequency and replacement time for the system when the lifetime distribution of a component follows the Weibull distribution. Numerical results are provided to show the application of the proposed model. In addition, a sensitivity analysis is performed to examine the effect of maintenance parameters on the model.

Item Type: Book section
DOI/Identification number: 10.1201/9781351174664-63
Subjects: H Social Sciences > HA Statistics > HA33 Management Science
Divisions: Divisions > Kent Business School - Division > Kent Business School (do not use)
Depositing User: Shaomin Wu
Date Deposited: 28 Jun 2018 06:45 UTC
Last Modified: 16 Feb 2021 13:55 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/67431 (The current URI for this page, for reference purposes)
Wu, Shaomin: https://orcid.org/0000-0001-9786-3213
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