IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v240y2026i3p882-902.html

A case study on multivariate statistical modeling for maintenance of freight railway wheelsets

Author

Listed:
  • Abhishek S. Bhadouria
  • Rajesh Prasad Mishra
  • Antonio R. Andrade

Abstract

This paper undertakes a linear mixed model analysis framework to explore the impact of maintenance decisions on the turning (re-profiling) of freight railway wheelsets. Univariate and multivariate linear models analyzed all the different variables (including fixed and random factors) that mainly impact maintenance and life cycle. The impact of maintenance is based on response variables such as changes in wheel diameter, in flange thickness, in flange height, and in the flange slope. All response variables are considered individually and combined to produce different statistical model specifications. The evaluation and comparison of the different model specifications are conducted with the Akaike Information Criterion (AIC). The results show that (i) the multivariate model is the most suitable and (ii) the overall AIC value of a multivariate model is lower than its sum for the univariate linear mixed models.

Suggested Citation

  • Abhishek S. Bhadouria & Rajesh Prasad Mishra & Antonio R. Andrade, 2026. "A case study on multivariate statistical modeling for maintenance of freight railway wheelsets," Journal of Risk and Reliability, , vol. 240(3), pages 882-902, June.
    • Handle: RePEc:sae:risrel:v:240:y:2026:i:3:p:882-902
    DOI: 10.1177/1748006X261427162
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X261427162
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1748006X261427162?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
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:sae:risrel:v:240:y:2026:i:3:p:882-902. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: SAGE Publications (email available below). General contact details of provider: .

    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.