Piezoelectric Sensor-Based Health Monitoring of Railroad Tracks Using a Two-Step Support Vector Machine Classifier
Publication: Journal of Infrastructure Systems
Volume 14, Issue 1
Abstract
A piezoelectric sensor-based health monitoring technique using a two-step support vector machine (SVM) classifier is developed for railroad track damage identification. A built-in active sensing system composed of two lead–zirconate–titanate patches was investigated in conjunction with both impedance and guided wave propagation methods to detect two kinds of damage in a railroad track (hole damage in diameter at the web section and transverse cut damage in length and in depth at the head section). Two damage-sensitive features were separately extracted from each method: (1) Feature I: root-mean-square deviations of impedance signatures; and (2) Feature II: sum of square of wavelet coefficients for maximum energy mode of guided waves. By defining appropriate damage indices from these two damage-sensitive features, a two-dimensional damage feature (2D DF) space was made. In order to enhance the damage identification capability of the current active sensing system, a two-step SVM classifier was applied to the 2D DF space. As a result, optimal separable hyperplanes were successfully established by the two-step SVM classifier: damage detection was accomplished by the first step SVM, and damage classification was carried out by the second step SVM. Finally, the applicability of the proposed two-step SVM classifier has been verified by 30 test patterns obtained in advance from the experimental study.
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Acknowledgments
This work was jointly supported by the Smart Infra-Structure Technology Center at KAIST, by the KSEFKorea Science and Engineering Foundation and the Infra-Structure Assessment Research Center, the Ministry of Construction and Transportation, Korea, and the Railway Tech Laboratories of the United States. This financial support is greatly appreciated.
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Information
Published In
Journal of Infrastructure Systems
Volume 14 • Issue 1 • March 2008
Pages: 80 - 88
Copyright
© 2008 ASCE.
History
Received: Jun 15, 2006
Accepted: Dec 14, 2006
Published online: Mar 1, 2008
Published in print: Mar 2008
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