Abstract
In this paper, an application of computer vision and machine learning algorithms for common crossing frog diagnostics is presented. The rolling surface fatigue of frogs along the crossing lifecycle is analysed. The research is based on information from high-resolution optical images of the frog rolling surface and images from magnetic particle inspection. Image processing methods are used to pre-process the images and to detect the feature set that corresponds to objects similar to surface cracks. Machine learning methods are used for the analysis of crack images from the beginning to the end of the crossing lifecycle. Statistically significant crack features and their combinations that depict the surface fatigue state are found. The research result consists of the early prediction of rail contact fatigue.
Highlights
Railway turnouts are high-asset and maintenance-intensive parts of the railway superstructure
The research is based on information from high-resolution optical images of the frog rolling surface and images from magnetic particle inspection
Machine learning methods are used for the analysis of crack images from the beginning to the end of the crossing lifecycle
Summary
Railway turnouts are high-asset and maintenance-intensive parts of the railway superstructure. Many different systems [6] are used for the inspection of common crossing rolling surfaces: profile, surface scan and video inspection, microstructure imaging, eddy current and ultrasound, vehicle-based and track-based inertial measurements (Fig. 1). Urban Rail Transit (2019) 5(2):123132 methods for common crossings at Scorpion and Lauros (Fig. 2) Both methods depict the wear state of the rolling surface, and the additional instantaneous high-resolution imaging is considered. A rail surface inspection method using deep learning and image processing is proposed in the study [17]. An early detection of common crossing rolling contact faults with vehicle-based inertial measurements and machine learning methods is studied in [20]. The aims of the present research are the objectification and automatization of the conventional human visual inspections, as well as discovering the possibilities for early prediction of rail contact fatigue in common crossings
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