An automated long range ultrasonic rail flaw detection system based on the support vector machine algorithm

This paper discusses a flaw detection system that uses automated long range ultrasonic detectors modeled on a successful pattern recognition method. The procedure has three stages: 1. a variety of feature extraction methods to better represent the signals; 2. feature ranking based on feature selection; and 3. classification using a kernel-based support vector machine (SVM). The results of analysis indicate that the proposed algorithm is successful at detecting flaws.

Language

  • English

Media Info

  • Media Type: Print
  • Features: Figures; References; Tables;
  • Pagination: pp 199-210
  • Monograph Title: Computers in Railways XIII. Computer System Design and Operation in the Railway and Other Transit Systems

Subject/Index Terms

Filing Info

  • Accession Number: 01486912
  • Record Type: Publication
  • ISBN: 9781845646165
  • Files: TRIS
  • Created Date: Jun 19 2013 1:50PM