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

Wang, L; An, M; Qin, Y; Jia, L

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.

Citation

Wang, L., An, M., Qin, Y., & Jia, L. (2018). A risk-based maintenance decision-making approach for railway asset management. International Journal of Software Engineering and Knowledge Engineering, 28(4), 453-483. https://doi.org/10.1142/s0218194018400065

• Journal Article Type: Article
• Acceptance Date: Apr 1, 2018
• Online Publication Date: Apr 26, 2018
• Publication Date: Apr 26, 2018
• Deposit Date: Nov 2, 2018
• Journal: International Journal of Software Engineering and Knowledge Engineering (ijseke)
• Print ISSN: 0218-1940
• Publisher: World Scientific Publishing
• Volume: 28
• Issue: 4
• Pages: 453-483
• DOI: https://doi.org/10.1142/s0218194018400065
• Publisher URL: https://doi.org/10.1142/s0218194018400065
• Related Public URLs: https://www.worldscientific.com/worldscinet/ijseke
• Additional Information: Funders : Engineering and Physical Sciences Research Council (EPSRC);State Key Laboratory of Rail Traffic Control and Safety of Beijing Jiaotong University; Projects : Application of fuzzy reasoning methodology to railway safety risk assessment process;Railway Infrastructure Safety Risk Quantitative Analysis Technology; Grant Number: GR/D066468; Grant Number: RCS2017K001 RCS2016ZT016