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Modeling Track Geometry Degradation Using Support Vector Machine Technique

[+] Author Affiliations
Can Hu, Xiang Liu

Rutgers, The State University of New Jersey, Piscataway, NJ

Paper No. JRC2016-5739, pp. V001T01A011; 6 pages
doi:10.1115/JRC2016-5739
From:
  • 2016 Joint Rail Conference
  • 2016 Joint Rail Conference
  • Columbia, South Carolina, USA, April 12–15, 2016
  • Conference Sponsors: Rail Transportation Division
  • ISBN: 978-0-7918-4967-5
  • Copyright © 2016 by ASME

abstract

Analyzing track geometry defects is of crucial importance for railway safety. Understanding when a defect will need to be repaired can help in both planning a preventive maintenance schedule and reducing the probability of track failures. This paper discusses the data cleaning and analysis processes for modeling track geometry degradation. An analytical data model named the Support Vector Machine (SVM) was developed to model the deterioration of track geometry defects. This paper mainly focuses on the following three defect types — surface, cross level and dip. The model accounts for traffic volume, defect amplitude, track class, speed and other potential factors. Results demonstrate that the proposed analytical data model can have a prediction accuracy above 70%.

Copyright © 2016 by ASME

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