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Maintenance processes modelling and optimisation

Zhang, Yang; Andrews, John; Reed, Sean; Karlberg, Magnus

Authors

Yang Zhang

JOHN ANDREWS john.andrews@nottingham.ac.uk
Professor of Infrastructure Asset Management

Sean Reed

Magnus Karlberg



Abstract

A Maintenance Procedure is conducted in order to prevent the failure of a system or to restore the functionality of a failed system. Such a procedure consists of a series of tasks, each of which has a distribution of times to complete and a probability of being performed incorrectly. The inclusion of tests can be used to identify any maintenance errors which have occurred. When an error is identified it can be addressed through a corresponding correction sequence which will have associated costs and add to the maintenance process completion time. A modified FMEA approach has been used to identify the possible tests. By incorporating any selection of tests into the maintenance process it can then analysed using a discrete-event simulation to predict the expected completion time distribution. The choice of tests to perform and when to do them is then made to successfully complete the maintenance objective in the shortest possible time using a genetic algorithm. The methodology is demonstrated by applying it to the repair process for a car braking system. The developed method is suitable for application in abroad range of industries.

Citation

Zhang, Y., Andrews, J., Reed, S., & Karlberg, M. (in press). Maintenance processes modelling and optimisation. Reliability Engineering and System Safety, https://doi.org/10.1016/j.ress.2017.02.011

Journal Article Type Article
Acceptance Date Feb 26, 2017
Online Publication Date Feb 27, 2017
Deposit Date Mar 8, 2017
Publicly Available Date Mar 8, 2017
Journal Reliability Engineering and System Safety
Print ISSN 0951-8320
Electronic ISSN 0951-8320
Publisher Elsevier
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1016/j.ress.2017.02.011
Keywords Maintenance; Optimisation; Failure mode and effect analysis; Discrete-event simulation; Genetic algorithm; System availability
Public URL http://eprints.nottingham.ac.uk/id/eprint/41160
Publisher URL http://www.sciencedirect.com/science/article/pii/S0951832016306342
Copyright Statement Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0





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