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Identification of Defective Railway Wheels from Highly Imbalanced Wheel Impact Load Detector Sensor Data

In: Operations Research Proceedings 2019

Author

Listed:
  • Sanjeev Sabnis

    (Indian Institute of Technology Bombay)

  • Shravana Kumar Yadav

    (Indian Institute of Technology Bombay)

  • Shripad Salsingikar

    (Indian Institute of Technology Bombay
    TATA Consultancy Services)

Abstract
The problem solving competition organized by the Railway Application Section of the Institute of Operations Research and Management Sciences (INFORMS) in 2017 was to predict the values of load exerted by wheels on the track, when a currently empty rail car would be loaded in the next trip. The organizers provided Wheel Impact Load Detector (WILD) data i.e. value of peak force along with other input variables such as train number, car number, axle side, wheel age, loaded or empty status etc. In this work, the original prediction problem is converted into a classification problem on the basis of peak force values in order to detect defects in railroad wheels. Peak force values greater than or equal to threshold value (≥ 90 Kilo Pound Force (kips)) define one class, while its values less than threshold value (

Suggested Citation

  • Sanjeev Sabnis & Shravana Kumar Yadav & Shripad Salsingikar, 2020. "Identification of Defective Railway Wheels from Highly Imbalanced Wheel Impact Load Detector Sensor Data," Operations Research Proceedings, in: Janis S. Neufeld & Udo Buscher & Rainer Lasch & Dominik Möst & Jörn Schönberger (ed.), Operations Research Proceedings 2019, pages 397-403, Springer.
    • Handle: RePEc:spr:oprchp:978-3-030-48439-2_48
    DOI: 10.1007/978-3-030-48439-2_48
    as

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