Finite-Element Study of Motion-Induced Eddy Current Array Method for High-Speed Rail Defects Detection

Abstract

Nondestructive testing (NDT) methods are widely used in the rail industry to detect and characterize rolling contact fatigue (RCF) defects in railroads, which is very important for railway inspection and maintenance to prevent catastrophic accidents. Existing NDT methods, e.g., ultrasonic testing (UT), magnetic flux leakage (MFL), and eddy current testing (ECT) have been successfully applied in the rail industry, and the state-of-the-art UT method reported recently achieved a high speed of 40 mi/h with a probability of detection (POD) over 80% under 30% false alarms. However, NDT methods still suffer from a bottleneck in that a higher inspection speed causes lower detection sensitivity due to their physical limits, such as negative velocity effect and long sensing time. One of the leading challenges to the rail NDT community is to develop a high-speed high-sensitivity (HSHS) capability that can provide an improved POD of rail defects in high-speed inspection scenarios over 60 mi/h. This article proposes a horizontal U-shaped magnets-based motion-induced eddy current array (MIECA) method to detect rail surface defects with the HSHS capability. The MIECA method deploys a three-axis magnetic sensor array along the rail transverse direction at the middle of the magnets to measure the MIECA signals, which utilizes the wake effect of the diffused motion-induced eddy current (MIEC) caused by the relative high-speed motion between the magnets and the rail track. Finite-element method (FEM) simulations with a wide speed range from 0 to 62.5 mi/h are carried out to investigate the relationships between the MIEC generation, diffusion and magnitude, and the three-axis MIECA signals. The simulation results show that the higher the speed, the greater the magnitude of diffused MIEC, and the greater the peak-to-peak values of three-axis MIECA signals caused by rail surface defects, which shows a great promise for detecting rail surface defects in high-speed scenarios and is superior inspection relative to existing NDT methods in terms of inspection speed, detection sensitivity, and defect characterization capability.