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Eddy Current Rail Inspection Using AC Bridge Techniques

Published

Author(s)

Andrew D. Koffman, Bryan C. Waltrip, Yicheng Wang

Abstract

AC bridge techniques commonly used for precision impedance measurements have been adopted to develop an eddy current sensor for rail defect detection. By using two detection coils instead of just one in a conventional sensor, we can balance out the large baseline signals corresponding to a normal rail. We have significantly enhanced the detection sensitivity of the eddy current method by detecting and demodulating the differential signal of the two coils induced by rail defects, using a digital lock-in amplifier algorithm. We have also explored to compensate for the lift-off effect of the eddy current sensor due to vibrations by using the summing signal of the detection coils to measure the lift-off distance. The dominant component of the summing signal is a constant resulting from direct coupling from the excitation coil which can be experimentally determined. The remainder of the summing signal, which decreases as the lift-off distance increases, is induced by the secondary eddy current. This dependence on the lift-off distance is used to calibrate the differential signal, allowing to more accurately characterize the defects. Simulated experiments on a sample rail have been performed using a computer controlled X-Y moving table with the X-axis mimicking train motion and the Y-axis mimicking train’s vibrational bumping. Experimental results demonstrate the effectiveness of the new detection method.
Citation
Journal of Research (NIST JRES) - 118.007
Report Number
118.007

Keywords

AC bridges, Demodulation, Digital lock-in amplifier, Eddy current rail inspection

Citation

Koffman, A. , Waltrip, B. and Wang, Y. (2013), Eddy Current Rail Inspection Using AC Bridge Techniques, Journal of Research (NIST JRES), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/jres.118.007 (Accessed December 21, 2024)

Issues

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Created February 26, 2013, Updated November 10, 2018