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Automatic defect classification in long-range ultrasonic rail inspection using a support vector machine-based smart system

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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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Document Type: Research Article

Affiliations: 1: Department of Civil and Geological Engineering at New Mexico State University, USA 2: Associate Professor at the Department of Structural Engineering of the University of California, San Diego, USA, and the Director of the NDE & Structural Health Monitoring Laboratory. He is a UCSD Hellman Faculty Fellow, a Fulbright Scholar and a Technical Editor of ASNT's Journal of Research in Nondestructive Evaluation 3: Track Research Division, Federal Railroad Administration, US Department of Transportation

Publication date: June 1, 2004

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