Author(s)

Lei Jia¹, Jee Woong Park¹, Ming Zhu², Yingtao Jiang², Lihao Qiu², Hualiang Teng¹

¹Department of Civil and Environmental Engineering and Construction, University of Nevada, Las Vegas, NV, USA.
²Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, NV, USA.

Rail defects, both internal and external, pose significant safety risks. Acoustic Emission (AE) technology has emerged as a promising method for monitoring damage progression and detecting rail defects. This research aims to investigate AE signal characteristics generated from external rail defects using both rail-mounted and vehicle-mounted settings. This investigation is expected to enhance our understanding of how AE signals correlate with defects and how effectively they can identify them. The results of the tests revealed two key findings. First, the AE detection rate is low, which suggests that AE detection rates may depend heavily on defect size and field conditions. Second, wavelet packet power (WPP) analysis indicates that the AE signals from external rail defects exhibited concentrated and intense peaks within the 100 - 160 kHz range. Overall, detecting external defects showed low performance due to indistinct AE signal characteristics and significant environmental noise. However, the application of continuous wavelet transforms (CWT) and wavelet packet power analysis presented more insights by identifying energy distributions and frequency peaks associated with external defects.

Railroad Infrastructure, Rail Defect Detection, Wavelet Analysis, Acoustic Emission Detection

Jia, L. , Park, J. , Zhu, M. , Jiang, Y. , Qiu, L. and Teng, H. (2025) Non-Contact Acoustic Emission Detection of External Rail Defects Using Air-Coupled Sensors. Journal of Transportation Technologies, 15, 560-577. doi: 10.4236/jtts.2025.154026.