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Optimizing maintenance decisions in railway wheelsets: A Markov decision process approach

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  • Joaquim AP Braga
  • António R Andrade

Abstract

This article models the decision problem of maintaining railway wheelsets as a Markov decision process, with the aim to provide a way to support condition-based maintenance for railway wheelsets. A discussion on the role of the railway wheelsets is provided, as well as some background on the technical standards that guide maintenance decisions. A practical example is explored with the estimation of Markov transition matrices for different condition states that depend on the wheelset diameter, its mileage since last turning action (or renewal) and the damage occurrence. Bearing in mind all the possible maintenance actions, an optimal strategy is achieved, providing a map of best actions depending on the current state of the wheelset.

Suggested Citation

  • Joaquim AP Braga & António R Andrade, 2019. "Optimizing maintenance decisions in railway wheelsets: A Markov decision process approach," Journal of Risk and Reliability, , vol. 233(2), pages 285-300, April.
    • Handle: RePEc:sae:risrel:v:233:y:2019:i:2:p:285-300
    DOI: 10.1177/1748006X18783403
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    References listed on IDEAS

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    1. Samer Madanat & Moshe Ben-Akiva, 1994. "Optimal Inspection and Repair Policies for Infrastructure Facilities," Transportation Science, INFORMS, vol. 28(1), pages 55-62, February.
    2. Gabrel, Virginie & Murat, Cécile & Thiele, Aurélie, 2014. "Recent advances in robust optimization: An overview," European Journal of Operational Research, Elsevier, vol. 235(3), pages 471-483.
    3. Papakonstantinou, K.G. & Shinozuka, M., 2014. "Planning structural inspection and maintenance policies via dynamic programming and Markov processes. Part II: POMDP implementation," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 214-224.
    4. Papakonstantinou, K.G. & Shinozuka, M., 2014. "Planning structural inspection and maintenance policies via dynamic programming and Markov processes. Part I: Theory," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 202-213.
    5. Zoubin Ghahramani, 2015. "Probabilistic machine learning and artificial intelligence," Nature, Nature, vol. 521(7553), pages 452-459, May.
    6. 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.
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    Cited by:

    1. 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).
    2. Mikhail, Mina & Ouali, Mohamed-Salah & Yacout, Soumaya, 2024. "A data-driven methodology with a nonparametric reliability method for optimal condition-based maintenance strategies," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

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