A method for in-field railhead crack detection using digital image correlation

• Railway infrastructure managers must decide when and how to maintain rails. However, they often have insufficient information about railhead cracks. Therefore, we propose a new method for rail crack detection using a train-mounted digital image correlation (DIC) camera system. The measurement train's weight cause rail bending, allowing the DIC to measure strain concentrations caused by surface-breaking cracks. In this study, we evaluate the method under laboratory conditions. The detected cracks correlate to the actual crack network in the analysed rail field sample. Furthermore, finite element simulations show the method's high sensitivity to crack depths. Existing methods, such as ultra-sonic and eddy-current, produce damage severity indications. The proposed method complements these techniques by providing a discrete description of the surface-breaking cracks and their depth. This information enables infrastructure managers to optimize rail maintenance. Additionally, such detailed measurements can be valuable for research in railhead damage evolution.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Maskinteknik -- Teknisk mekanik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Mechanical Engineering -- Applied Mechanics (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Medicinteknik -- Medicinsk laboratorie- och mätteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Medical Engineering -- Medical Laboratory and Measurements Technologies (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Medicinteknik -- Annan medicinteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Medical Engineering -- Other Medical Engineering (hsv//eng)

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)

Keyword

safety

railway maintenance

crack detection

digital image correlation

Conditioning monitoring

Publication and Content Type

art (subject category)

ref (subject category)