Author： McNamara J D di Scalea F Lanza Fateh M
Publisher： The British Institute of Non-Destructive Testing
ISSN： 1354-2575
Source： Insight - Non-Destructive Testing and Condition Monitoring, Vol.46, Iss.6, 2004-06, pp. : 331-337
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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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Insight - Non-Destructive Testing and Condition Monitoring, Vol. 47, Iss. 6, 2005-06 ,pp. :