IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v242y2015i3p975-986.html
   My bibliography  Save this article

Optimizing the re-profiling strategy of metro wheels based on a data-driven wear model

Author

Listed:
  • Wang, Ling
  • Xu, Hong
  • Yuan, Hua
  • Zhao, Wenjie
  • Chen, Xiai

Abstract

The wheels are one of the most worn components on a train. When the wear is unacceptable, the re-profiling can restore the shape of the wheel flange with the cost of decreasing the wheel diameter. The decision of re-profiling has serious implications for the life span of wheels. In this paper, based on the analysis of the wear and re-profiling characteristics of metro wheels, a data-driven model of the relationship between the wheel diameter, the flange thickness, their wear rates, and the re-profiling gain is built for the wheels of Guangzhou Metro Line One. A (SdP, SdR) re-profiling strategy is proposed, where SdP is the wheel flange thickness threshold to trigger a preventive re-profiling and SdR is the wheel flange thickness after the preventive re-profiling. Then the Monte Carlo simulation model of the re-profiling strategy is described in this paper. To find out when a re-profiling should be performed in terms of the flange wear-out level and what values of the flange thickness should be brought to by re-profiling, the simulation results for optimizing the decision variables (SdP, SdR) of the re-profiling strategy are given in this paper. Those having longer life spans are listed as the preferred re-profiling strategies. The study in this paper reveals that the wear rate of the flange thickness is correlated with the flange thickness, while the diameter wear rate could be considered independent of the flange thickness in terms of the wheels of Guangzhou Metro Line One. On the other hand, based on the observation and analysis of an available sample set from Guangzhou Metro Line One, the re-profiling gain is dependent on the flange thickness before or after re-profiling. The preferred re-profiling strategies suggested by this study can increase the life span comparing with the existing re-profiling strategies based on the simulation. The models and methods presented in this paper could benefit both city metro companies and inter-city rail companies by prolonging the life span of rolling stock wheels.

Suggested Citation

  • Wang, Ling & Xu, Hong & Yuan, Hua & Zhao, Wenjie & Chen, Xiai, 2015. "Optimizing the re-profiling strategy of metro wheels based on a data-driven wear model," European Journal of Operational Research, Elsevier, vol. 242(3), pages 975-986.
    • Handle: RePEc:eee:ejores:v:242:y:2015:i:3:p:975-986
    DOI: 10.1016/j.ejor.2014.10.033
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221714008546
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2014.10.033?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mercier, Sophie & Pham, Hai Ha, 2012. "A preventive maintenance policy for a continuously monitored system with correlated wear indicators," European Journal of Operational Research, Elsevier, vol. 222(2), pages 263-272.
    2. Scarf, Philip A., 1997. "On the application of mathematical models in maintenance," European Journal of Operational Research, Elsevier, vol. 99(3), pages 493-506, June.
    3. Pham, Hoang & Wang, Hongzhou, 1996. "Imperfect maintenance," European Journal of Operational Research, Elsevier, vol. 94(3), pages 425-438, November.
    4. Taghipour, Sharareh & Banjevic, Dragan, 2012. "Optimal inspection of a complex system subject to periodic and opportunistic inspections and preventive replacements," European Journal of Operational Research, Elsevier, vol. 220(3), pages 649-660.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Andrade, Antonio Ramos & Stow, Julian, 2017. "Assessing the potential cost savings of introducing the maintenance option of ‘Economic Tyre Turning’ in Great Britain railway wheelsets," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 317-325.
    2. Zengqiang Jiang & Dragan Banjevic & Mingcheng E & Bing Li, 2017. "Optimizing the re-profiling policy regarding metropolitan train wheels based on a semi-Markov decision process," Journal of Risk and Reliability, , vol. 231(5), pages 495-507, October.
    3. Braga, Joaquim A.P. & Andrade, António R., 2021. "Multivariate statistical aggregation and dimensionality reduction techniques to improve monitoring and maintenance in railways: The wheelset component," Reliability Engineering and System Safety, Elsevier, vol. 216(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Guo R. & Ascher H. & Love E., 2001. "Towards Practical and Synthetical Modelling of Repairable Systems," Stochastics and Quality Control, De Gruyter, vol. 16(1), pages 147-182, January.
    2. Kurt, Murat & Kharoufeh, Jeffrey P., 2010. "Optimally maintaining a Markovian deteriorating system with limited imperfect repairs," European Journal of Operational Research, Elsevier, vol. 205(2), pages 368-380, September.
    3. Alaswad, Suzan & Xiang, Yisha, 2017. "A review on condition-based maintenance optimization models for stochastically deteriorating system," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 54-63.
    4. Pascual, R. & Meruane, V. & Rey, P.A., 2008. "On the effect of downtime costs and budget constraint on preventive and replacement policies," Reliability Engineering and System Safety, Elsevier, vol. 93(1), pages 144-151.
    5. Wang, Hongzhou, 2002. "A survey of maintenance policies of deteriorating systems," European Journal of Operational Research, Elsevier, vol. 139(3), pages 469-489, June.
    6. Chaoqun Duan & Chao Deng & Bingran Wang, 2019. "Multi-phase sequential preventive maintenance scheduling for deteriorating repairable systems," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1779-1793, April.
    7. Wu, Shaomin & Clements-Croome, Derek, 2005. "Preventive maintenance models with random maintenance quality," Reliability Engineering and System Safety, Elsevier, vol. 90(1), pages 99-105.
    8. Taghipour, Sharareh & Banjevic, Dragan, 2012. "Optimal inspection of a complex system subject to periodic and opportunistic inspections and preventive replacements," European Journal of Operational Research, Elsevier, vol. 220(3), pages 649-660.
    9. Zhang, Nan & Fouladirad, Mitra & Barros, Anne, 2018. "Optimal imperfect maintenance cost analysis of a two-component system with failure interactions," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 24-34.
    10. Cavalcante, Cristiano A.V. & Lopes, Rodrigo S. & Scarf, Philip A., 2021. "Inspection and replacement policy with a fixed periodic schedule," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    11. Peters, Lennart & Madlener, Reinhard, 2017. "Economic evaluation of maintenance strategies for ground-mounted solar photovoltaic plants," Applied Energy, Elsevier, vol. 199(C), pages 264-280.
    12. Rajkumar Bhimgonda Patil & Basavraj S Kothavale & Laxman Yadu Waghmode, 2019. "Selection of time-to-failure model for computerized numerical control turning center based on the assessment of trends in maintenance data," Journal of Risk and Reliability, , vol. 233(2), pages 105-117, April.
    13. Xiang, Yisha, 2013. "Joint optimization of X¯ control chart and preventive maintenance policies: A discrete-time Markov chain approach," European Journal of Operational Research, Elsevier, vol. 229(2), pages 382-390.
    14. Izquierdo, J. & Márquez, A. Crespo & Uribetxebarria, J. & Erguido, A., 2020. "On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects," Renewable Energy, Elsevier, vol. 153(C), pages 1100-1110.
    15. Seyed Habib A. Rahmati & Abbas Ahmadi & Kannan Govindan, 2018. "A novel integrated condition-based maintenance and stochastic flexible job shop scheduling problem: simulation-based optimization approach," Annals of Operations Research, Springer, vol. 269(1), pages 583-621, October.
    16. Raouf, BOUCEKKINE & Blanca, MARTINEZ & Cagri, SAGLAM, 2006. "Capital Maintenance Vs Technology Adopton under Embodied Technical Progress," Discussion Papers (ECON - Département des Sciences Economiques) 2006030, Université catholique de Louvain, Département des Sciences Economiques.
    17. Dewan, Isha & Dijoux, Yann, 2015. "Modelling repairable systems with an early life under competing risks and asymmetric virtual age," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 215-224.
    18. Zhengxin Zhang & Xiaosheng Si & Changhua Hu & Xiangyu Kong, 2015. "Degradation modeling–based remaining useful life estimation: A review on approaches for systems with heterogeneity," Journal of Risk and Reliability, , vol. 229(4), pages 343-355, August.
    19. Zhicheng Zhu & Yisha Xiang & Bo Zeng, 2021. "Multicomponent Maintenance Optimization: A Stochastic Programming Approach," INFORMS Journal on Computing, INFORMS, vol. 33(3), pages 898-914, July.
    20. Xiao, Lei & Zhang, Xinghui & Tang, Junxuan & Zhou, Yaqin, 2020. "Joint optimization of opportunistic maintenance and production scheduling considering batch production mode and varying operational conditions," Reliability Engineering and System Safety, Elsevier, vol. 202(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:242:y:2015:i:3:p:975-986. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.