A built-in active sensing system-based structural health monitoring technique using statistical pattern recognition

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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 PZT 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 0.5cm in diameter at the web section and transverse cut damage 7.5cm in length and 0.5cm in depth at the head section). Two damage-sensitive features were separately extracted from each method: a) feature 1: root mean square deviations (RMSD) of impedance signatures, and b) feature II : sum of square of wavelet coefficients for maximum energy mode of guided waves. By defining damage indices from these two damagesensitive features, a two-dimensional damage feature (2-D 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 2-D DF space. As a result, optimal separable hyper-planes (OSH) 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 thirty test patterns prepared in advance from the intact state and two damage states.
Publisher
KOREAN SOC MECHANICAL ENGINEERS
Issue Date
2007-06
Language
English
Article Type
Article; Proceedings Paper
Citation

JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, v.21, pp.896 - 902

ISSN
1738-494X
URI
http://hdl.handle.net/10203/7664
Appears in Collection
CE-Journal Papers(저널논문)
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