Visualization of the Rolling Contact Fatigue Cracks in Rail Tracks with a Magnetooptical Sensor

• Published in: Journal of nondestructive evaluation Vol. 38; no. 3; pp. 1 - 8
• Main Authors: Chotzoglou, A., Pissas, M., Zervaki, A. D., Haidemenopoulos, G. N., Pissas, T.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2019; Springer Nature B.V
• Subjects: Algorithms; Carbon steels; Cementite; Characterization and Evaluation of Materials; Classical Mechanics; Computer vision; Control; Crack propagation; Cracks; Dynamical Systems; Engineering; Fatigue cracks; Fatigue failure; Ferromagnetic materials; Flaw detection; Magnetic fields; Magnetic permeability; Magnetism; Microstructure; Pearlite; Polarized light; Rolling contact; Sensors; Solid Mechanics; Structural steels; Vibration; Computer vision; Rolling contact fatigue; Rail head check; Rail tracks; Magnetooptic sensor
• ISSN: 0195-9298; 1573-4862
• DOI: 10.1007/s10921-019-0606-5

The rolling contact fatigue cracks (RCF), produced at the surface of the rail tracks, can be detected using a magnetooptical (MO) sensor. Rail tracks are carbon steels with pearlite microstructure. This microstructure has a lamellar texture composed of alternating layers of ferrite and cementite. Both phases are soft ferromagnetic materials at room temperature. If an external magnetic field is applied on the surface of a rail track, the reduced magnetic permeability causes a magnetic leakage field above the cracks. When the external magnetic field is removed, in most cases, a residual stray magnetic field remains above the cracks. When a MO sensor is placed on the surface of the rail track, the sudden change of the stray remanent magnetic field near a crack, yields a significant rotation of the polarization plane of the reflected light, resulting in high MO contrast, exactly above the cracks. Using a polished surface and a cross-section from the head of the rail track, we succeeded in visualizing the RCF cracks in the laboratory. The RCF cracks can also be detected on the surface of the rail track, in field measurements, using a portable commercial polarized light microscope equipped with a MO sensor. Finally, we use computer vision methods, to automatically detect the RCF cracks, using video recorded by displacing the portable microscopy with the MO sensor, on the surface of the rail tracks. We tested an unsupervised automatic crack detection algorithm, which exploits the tubular contrast of the RCF cracks to pinpoint the pixels that correspond to them.