Abstract
This paper presents the results from a pilot study of a 'smart system' used for defect detection in railroad rails, particularly the critical transverse-type defects. The experimental data used to train the pattern recognition 'smart system' were extracted from experiments conducted during a previous long-range ultrasonic guided wave study conducted at the University of California, San Diego. Reflection coefficient plots corresponding to a variety of transverse and oblique defects were shown to provide features that were successfully used to train a 'smart system' to identify the defects automatically. This paper presents a brief introduction to support vector machines, followed by a description of the procedure used to determine the best data to be used to train the 'smart system', and concludes with lessons learned during this study.
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