A two-dimensional ordinary state-based peridynamic model for surface fatigue crack propagation in railheads

Based on ordinary state-based peridynamic theory, a 2D peridynamic model has been established to investigate fatigue crack propagation in railheads. The proposed model is verified in terms of rail deformation under a quasi-static load and the ductile material-related fatigue failure model. Good agreements have been achieved between a finite...

A comparative study of point clouds semantic segmentation using three different neural networks on the railway station dataset

Point cloud data have rich semantic representations and can benefit various applications towards a digital twin. However, they are unordered and anisotropically distributed, thus being unsuitable for a typical Convolutional Neural Networks (CNN) to handle. With the advance of deep learning, several neural networks claim to have solved the...

Automatic Defect Detection of Fasteners on the Catenary Support Device Using Deep Convolutional Neural Network

The excitation and vibration triggered by the long-term operation of railway vehicles inevitably result in defective states of catenary support devices. With the massive construction of high-speed electrified railways, automatic defect detection of diverse and plentiful fasteners on the catenary support device is of great significance for...