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Classification of Rail Switch Data Using Machine Learning Techniques

[+] Author Affiliations
Kaylen J. Bryan, Mitchell Solomon, Anthony O. Smith, Adrian M. Peter

Florida Institute of Technology, Melbourne, FL

Emily Jensen

Case Western Reserve University, Cleveland, OH

Christina Coley

East Carolina University, Greenville, NC

Kailas Rajan

University of Southern California, Los Angeles, CA

Charlie Tian

University of California, Berkeley, Berkeley, CA

Nenad Mijatovic, James M. Kiss

Alstom Signaling Operations LLC, Melbourne, FL

Benjamin Lamoureux, Pierre Dersin

Alstom Signaling Operations LLC, Paris, France

Paper No. JRC2018-6175, pp. V001T04A005; 10 pages
doi:10.1115/JRC2018-6175
From:
  • 2018 Joint Rail Conference
  • 2018 Joint Rail Conference
  • Pittsburgh, Pennsylvania, USA, April 18–20, 2018
  • Conference Sponsors: Rail Transportation Division
  • ISBN: 978-0-7918-5097-8
  • Copyright © 2018 by ASME

abstract

Rail switches are critical infrastructure components of a railroad network, that must maintain high-levels of reliable operation. Given the vast number and variety of switches that can exist across a rail network, there is an immediate need for robust automated methods of detecting switch degradations and failures without expensive add-on equipment. In this work, we explore two recent machine learning frameworks for classifying various switch degradation indicators: (1) a featureless recurrent neural network called a Long Short-Term Memory (LSTM) architecture, and (2), the Deep Wavelet Scattering Transform (DWST), which produces features that are locally time invariant and stable to time-warping deformations. We describe both methods as they apply to rail switch monitoring and demonstrate their feasibility on a dataset captured under the service conditions by Alstom Corporation. For multiple categories of degradation types, the baseline models consistently achieve near-perfect accuracies and are competitive with the manual analysis conducted by human switch-maintenance experts.

Copyright © 2018 by ASME
Topics: Machinery , Rails , Switches

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