It is essential to assess the rolling contact fatigue (RCF) of turnouts and maintain them in advance. It saves a lot of money while protecting the safety of railway operations. In Germany, the damage on rails, especially crossing noses, mainly depends on the subjective judgment of experts. There are no objective and comprehensive evaluation criteria. This paper presents the application of image processing and supervised machine learning algorithms to crossing nose fatigue judgment. The fatigue characteristics of the crossing nose rolling contact surface along the life cycle of the crossing nose are analyzed. The study used crack information from magnetic particle inspection (MPI) images of crossing nose surfaces. It uses basic image processing methods to collect physical information about features of fatigue cracks in images. Existing feature selection methods are used to exclude irrelevant features and retain valuable features. And we select the best feature selection method through the regression results. Statistically significant crack features and combinations that depict the surface fatigue state are found. In this paper, by comparing several usually machine learning regression algorithms, it is found that the supervised learning of support vector machine regression (SVR) has achieved the best results in the regression fitting of the crack feature data in this paper. The regression results form a simple system to evaluate the life cycle of crossing nose. The system finds the location of cracks that can create dangerous defects in the crossing nose surface. The research result consists of the early prediction of rail contact fatigue.

The share of rail transport in world transport continues to rise. As the number of trains increases, so does the load on the railway. The rails are in direct contact with the loaded wheels. Therefore, it is more easily damaged. In recent years, domestic and foreign scholars have conducted in-depth research on railway damage detection. As the weakest part of the track system, switches are more prone to damage. Assessing and predicting rail surface damage can improve the safety of rail operations and allow for proper planning and maintenance to reduce capital expenditure and increase operational efficiency. Under the premise that functional safety is paramount, predicting the service life of rails, especially turnouts, can significantly reduce costs and ensure the safety of railway transportation. This paper understands the evolution of contact fatigue on crossing noses through long-term observation and sampling of crossing noses in turnouts. The authors get images from new to damaged. After image preprocessing, MPI (Magnetic Particle Imaging) is divided into blocks containing local crack information. The obtained local texture information is used for regression prediction using machine-supervised learning and LSTM network (Long Short-Term Memory) methods. Finally, a technique capable of thoroughly evaluating the wear process of crossing noses is proposed.

Railway damage detection is of great significance in ensuring railway safety. The cracks on the rail surface play a key role in studying the formation and development process of rail damage, predicting the occurrence of rail defects, and then improving the service life of the rail. However, due to the small shape of the cracks, the typical detection method is relatively complicated, and the speed is quite slow. Although traditional magnetic particle inspection technology is fairly accurate at detection, it is costly and inconvenient to carry and install, while also limiting the detection speed and affecting the system’s operation. In this paper, a semantic segmentation detection method is developed by using various collected rail surface crack data and deep learning through a neural network. By comparing the inspection of the same rail surface with magnetic particle inspection technology, only inexpensive cameras are used and the inspection speed is increased while maintaining relatively high accuracy. In addition, the method can achieve fast detection speeds if it is extended to be combined with high-frequency cameras. It is an economical, efficient, and environmentally friendly method for future rail surface detection.