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

Sanjeev Sabnis (sabnissanjeev@gmail.com), Shravana Kumar Yadav and Shripad Salsingikar
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Sanjeev Sabnis: Indian Institute of Technology Bombay
Shravana Kumar Yadav: Indian Institute of Technology Bombay
Shripad Salsingikar: Indian Institute of Technology Bombay

A chapter in Operations Research Proceedings 2019, 2020, pp 397-403 from Springer

Abstract: 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 (

Keywords: Railway; Wheel impact load detectors; Zero-inflated binomial regression; Regularization (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/978-3-030-48439-2_48

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