Prediction of Rail Contact Fatigue on Crossings Using Image Processing and Machine Learning Methods

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  • Additional Information
    • Publication Information:
      SpringerOpen, 2019.
    • Publication Date:
      2019
    • Collection:
      LCC:Transportation engineering
      LCC:Transportation and communications
    • Abstract:
      Abstract In this paper, an application of computer vision and machine learning algorithms for common crossing frog diagnostics is presented. The rolling surface fatigue of frogs along the crossing lifecycle is analysed. The research is based on information from high-resolution optical images of the frog rolling surface and images from magnetic particle inspection. Image processing methods are used to pre-process the images and to detect the feature set that corresponds to objects similar to surface cracks. Machine learning methods are used for the analysis of crack images from the beginning to the end of the crossing lifecycle. Statistically significant crack features and their combinations that depict the surface fatigue state are found. The research result consists of the early prediction of rail contact fatigue.
    • File Description:
      electronic resource
    • ISSN:
      2199-6687
      2199-6679
    • Relation:
      http://link.springer.com/article/10.1007/s40864-019-0105-0; https://doaj.org/toc/2199-6687; https://doaj.org/toc/2199-6679
    • Accession Number:
      10.1007/s40864-019-0105-0
    • Rights:
      Journal Licence: CC BY
    • Accession Number:
      edsdoj.14f350dd50504f7991994d98b89bf27a
  • Citations
    • ABNT:
      MYKOLA SYSYN et al. Prediction of Rail Contact Fatigue on Crossings Using Image Processing and Machine Learning Methods. Urban Rail Transit, [s. l.], n. 2, p. 123, 2019. Disponível em: . Acesso em: 23 ago. 2019.
    • AMA:
      Mykola Sysyn, Ulf Gerber, Olga Nabochenko, Dmitri Gruen, Franziska Kluge. Prediction of Rail Contact Fatigue on Crossings Using Image Processing and Machine Learning Methods. Urban Rail Transit. 2019;(2):123. doi:10.1007/s40864-019-0105-0.
    • APA:
      Mykola Sysyn, Ulf Gerber, Olga Nabochenko, Dmitri Gruen, & Franziska Kluge. (2019). Prediction of Rail Contact Fatigue on Crossings Using Image Processing and Machine Learning Methods. Urban Rail Transit, (2), 123. https://doi.org/10.1007/s40864-019-0105-0
    • Chicago/Turabian: Author-Date:
      Mykola Sysyn, Ulf Gerber, Olga Nabochenko, Dmitri Gruen, and Franziska Kluge. 2019. “Prediction of Rail Contact Fatigue on Crossings Using Image Processing and Machine Learning Methods.” Urban Rail Transit, no. 2: 123. doi:10.1007/s40864-019-0105-0.
    • Harvard:
      Mykola Sysyn et al. (2019) ‘Prediction of Rail Contact Fatigue on Crossings Using Image Processing and Machine Learning Methods’, Urban Rail Transit, (2), p. 123. doi: 10.1007/s40864-019-0105-0.
    • Harvard: Australian:
      Mykola Sysyn, Ulf Gerber, Olga Nabochenko, Dmitri Gruen & Franziska Kluge 2019, ‘Prediction of Rail Contact Fatigue on Crossings Using Image Processing and Machine Learning Methods’, Urban Rail Transit, no. 2, p. 123, viewed 23 August 2019, .
    • MLA:
      Mykola Sysyn, et al. “Prediction of Rail Contact Fatigue on Crossings Using Image Processing and Machine Learning Methods.” Urban Rail Transit, no. 2, 2019, p. 123. EBSCOhost, doi:10.1007/s40864-019-0105-0.
    • Chicago/Turabian: Humanities:
      Mykola Sysyn, Ulf Gerber, Olga Nabochenko, Dmitri Gruen, and Franziska Kluge. “Prediction of Rail Contact Fatigue on Crossings Using Image Processing and Machine Learning Methods.” Urban Rail Transit, no. 2 (2019): 123. doi:10.1007/s40864-019-0105-0.
    • Vancouver/ICMJE:
      Mykola Sysyn, Ulf Gerber, Olga Nabochenko, Dmitri Gruen, Franziska Kluge. Prediction of Rail Contact Fatigue on Crossings Using Image Processing and Machine Learning Methods. Urban Rail Transit [Internet]. 2019 [cited 2019 Aug 23];(2):123. Available from: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsdoj&AN=edsdoj.14f350dd50504f7991994d98b89bf27a&custid=s8280428