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Advances in Civil Engineering
Volume 2010 (2010), Article ID 291293, 13 pages
http://dx.doi.org/10.1155/2010/291293
Research Article

Ultrasonic Guided Waves-Based Monitoring of Rail Head: Laboratory and Field Tests

1Department of Civil and Environmental Engineering, University of Pittsburgh, 949 Benedum Hall, 3700 O'Hara Street, Pittsburgh, PA 15261, USA
2Department of Structural and Geotechnical Engineering, University of Palermo, Viale delle Scienze, 90128 Palermo, Italy
3NDE and Structural Health Monitoring Laboratory, Department of Structural Engineering, University of California, San Diego 9500 Gilman Drive, M.C. 0085, La Jolla, CA 92093-0085, USA

Received 23 December 2009; Accepted 19 April 2010

Academic Editor: Jinying Zhu

Copyright © 2010 Piervincenzo Rizzo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Recent train accidents have reaffirmed the need for developing a rail defect detection system more effective than that currently used. One of the most promising techniques in rail inspection is the use of ultrasonic guided waves and noncontact probes. A rail inspection prototype based on these concepts and devoted to the automatic damage detection of defects in rail head is the focus of this paper. The prototype includes an algorithm based on wavelet transform and outlier analysis. The discrete wavelet transform is utilized to denoise ultrasonic signals and to generate a set of relevant damage sensitive data. These data are combined into a damage index vector fed to an unsupervised learning algorithm based on outlier analysis that determines the anomalous conditions of the rail. The first part of the paper shows the prototype in action on a railroad track mock-up built at the University of California, San Diego. The mock-up contained surface and internal defects. The results from three experiments are presented. The importance of feature selection to maximize the sensitivity of the inspection system is demonstrated here. The second part of the paper shows the results of field testing conducted in south east Pennsylvania under the auspices of the U.S. Federal Railroad Administration.