Eddy Currents Assessment of Rail Cracks Using Artificial Neural Networks in a Laboratory Setup

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Marcelo Gutiérrez
Javier Fava, PhD
Juan Vorobioff, PhD
Federico Checozzi
Marta Ruch, PhD
Tomás Di fiore


Although flaws associated with rolling contact fatigue (RCF) and the corresponding traffic induced damage, which are a cause of failure in railways, have been of great concern in railway system maintenance and safety strategies in many countries for at least two decades, this serious problem has not been yet adequately tackled in the Argentine railway system. The present upgrading activities undertaken in the Argentine railway system (in infrastructure and in rolling stock) are prompting the need for R&D in non-destructive testing techniques and procedures, to satisfy requirements of the new rolling stock and to ensure safe and economic operation of passengers and cargo. RCF damage appears as surface and near surface defects and grows into cracks which in time will propagate along the running surface and through the cross-section. Eddy current testing (ET) is a very efficient in-service inspection method for this task, the near field technique being especially recommended for ferromagnetic components.

In the present paper, an artificial neural network (ANN) method for automatic classification of flaws with lift-off compensation is presented and tested. The tests consist on the ET evaluation of right angle artificial cracks on a rail calibration coupon; the depth of the cracks studied ranged from 1 to 7 mm. The technique permitted to compensate the weakening of the signals caused by the lift-off effect, allowing signal cracks classification with lift-off variations of up to 5.4 mm.

The effect of crack skewness on the ET signals is also studied. Because the RCF cracks penetrate the rail at oblique angles, (10° to 30° to the rolling surface), an additional uncertainty component is added to the experiments if calibration is made with a piece having perpendicular cracks. In order to estimate this additional uncertainty on the ANN method presented here, further tests were made with a second calibration piece with cracks at 25° to the surface. Comparison of results showed that the peak to peak amplitudes for both types of cracks are not equivalent at all the tested depths.

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How to Cite
Gutiérrez, M., Fava, J., Vorobioff, J., Checozzi, F., Ruch, M., & Di fiore, T. (2023). Eddy Currents Assessment of Rail Cracks Using Artificial Neural Networks in a Laboratory Setup. Journal of Applied Research and Technology, 21(5), 730–741. https://doi.org/10.22201/icat.24486736e.2023.21.5.1624
Author Biography

Marcelo Gutiérrez, National Technological University - Haedo Regional Faculty

Electronics Engineer. PhD student in Engineering (Signals & Image Processing) at National Technological University – Buenos Aires Regional Faculty also called UTN-FRBA; My doctoral thesis topic is “Processing of eddy current signals applied to the inspection of railway rails and flat ferromagnetic components”. Research Professor at the National Technological University. My fields of interest, analog electronics, embedded systems, robotics, digital signal processing , data analysis, pattern recognition, artificial neural network and NDT.