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A method for in-field railhead crack detection using digital image correlation

Author

Listed:
  • Knut Andreas Meyer
  • Daniel Gren
  • Johan Ahlström
  • Anders Ekberg

Abstract

Railway infrastructure managers must decide when and how to maintain rails. However, they often have insufficient information about railhead cracks. Therefore, we propose a new method for rail crack detection using a train-mounted digital image correlation (DIC) camera system. The measurement train’s weight cause rail bending, allowing the DIC to measure strain concentrations caused by surface-breaking cracks. In this study, we evaluate the method under laboratory conditions. The detected cracks correlate to the actual crack network in the analysed rail field sample. Furthermore, finite element simulations show the method’s high sensitivity to crack depths. Existing methods, such as ultra-sonic and eddy-current, produce damage severity indications. The proposed method complements these techniques by providing a discrete description of the surface-breaking cracks and their depth. This information enables infrastructure managers to optimize rail maintenance. Additionally, such detailed measurements can be valuable for research in railhead damage evolution.

Suggested Citation

  • Knut Andreas Meyer & Daniel Gren & Johan Ahlström & Anders Ekberg, 2022. "A method for in-field railhead crack detection using digital image correlation," International Journal of Rail Transportation, Taylor & Francis Journals, vol. 10(6), pages 675-694, November.
    • Handle: RePEc:taf:tjrtxx:v:10:y:2022:i:6:p:675-694
    DOI: 10.1080/23248378.2021.2021455
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