Article
Optimizing AE-Sensor Position for Enhanced Rail Defect Detection (en)
* Presenting author
Abstract:
Ensuring the safety and reliability of railway systems is of highest importance, with nondestructive testing playing a crucial role. Acoustic emissions (AE) generated by growing defects in rails, including surface cracks, fractures, and internal fissures, can serve as valuable indicators for condition monitoring. Since rails are waveguides with low energy losses, the high-frequency AE signals can be observed over long distances. Despite this potential, a significant challenge arises from the high background noise level due to the rolling noise. To address this challenge, various signal processing techniques have been used in previous research. In contrast, this work focuses on optimizing the AE-sensor position based on the defect's location and source mechanism. This optimization is achieved through high-frequency Waveguide Finite Element simulations and an analysis of the cross-sectional mode shapes. This study aims for enhancing the effectiveness of nondestructive rail testing.