A risk-based maintenance decision-making approach for railway asset management

Wang, L, An, M ORCID: https://orcid.org/0000-0002-1069-7492, Qin, Y and Jia, L 2018, 'A risk-based maintenance decision-making approach for railway asset management' , International Journal of Software Engineering and Knowledge Engineering (ijseke), 28 (4) , pp. 453-483.

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Abstract

This paper presents a risk-based maintenance decision making modeling methodology for railway asset maintenance optimization, which takes risk and maintenance cost objectives into consideration in the decision making process. A bottom-up risk analysis approach has been developed by using fuzzy reasoning approach (FRA) and fuzzy-analytical hierarchy process (Fuzzy-AHP) to produce a risk model. A total cost model has also been developed to estimate repair/renewal, maintenance and performance review costs. A risk-based maintenance decision making support model has then been developed by integrating the risk model with cost model in which multi-criteria decision making (MCDM) techniques are employed to process the proposed risk-based maintenance decision making support model. An illustrative example on a section of a track system maintenance decision selection is used to demonstrate the application of the proposed methodology. The results show that by using the proposed methodology the qualitative and quantitative risk data and information with maintenance costs associated with railway assets can be evaluated efficiently and effectively, which provide very useful information to railway engineers, managers, and decision makers.

Item Type:	Article
Schools:	Schools > School of the Built Environment
Journal or Publication Title:	International Journal of Software Engineering and Knowledge Engineering (ijseke)
Publisher:	World Scientific Publishing
ISSN:	0218-1940
Related URLs:	Publisher
Funders:	Engineering and Physical Sciences Research Council (EPSRC), State Key Laboratory of Rail Traffic Control and Safety of Beijing Jiaotong University
Depositing User:	Professor Min An
Date Deposited:	02 Nov 2018 09:15
Last Modified:	16 Feb 2022 00:04
URI:	https://usir.salford.ac.uk/id/eprint/48694

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