Rail Surface Defect Detection and Analysis Using Multi-Channel Eddy Current Method Based Algorithm for Defect Evaluation

Abstract

The railroad rail support trains and contributes to their operation. Internal and surface defects occur on the rail due to various combinations of causes including fatigue loading and cyclic tension and compression among others from the deterioration of the rail along with the temperature differences of seasonal changes. Surface defects such as head check, shelling, and squats start out in the rail head and become internal defects due to poor maintenance, ultimately resulting in rail failure. In order to prevent rail failure, it is important that defects are identified through nondestructive evaluation (NDE) in advance and to carry out maintenance techniques including grinding. NDE methods include MFL, EMAT, and ECT, and among these, the ECT method is a representative method with excellent detection sensitivity that nondestructively inspects metal surfaces such as rails and pipes using an electromagnetic field. Also, since the defect signal is obtained as an electrical signal, the depth, length, and width of defects can be assessed using a defect evaluation algorithm. This study investigated the field applicability and future practical use of the 16 channel eddy current testing equipment and defect evaluation algorithm developed in this study. Therefore, the field applicability of the equipment and defect evaluation algorithm was investigated through the detection of artificial defects with varying size and depth. Afterwards, future practical use was evaluated by inspection of areas of rail that are in use and with naturally occurring surface defects and analysis of their size (length, width), depth, and phenomena.