Piezoelectric sensor-based health monitoring of railroad tracks using a two-step support vector machine classifier

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 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: (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.