Jianmei Wei

Wheel burn can affect the wheel–rail contact state and ride quality. With long-term operation, it can cause rail head spalling or transverse cracking, which will lead to rail breakage. By analyzing the relevant literature on wheel burn, this paper reviews the characteristics, mechanism of formation, crack extension, and NDT methods of wheel burn. The results are as follows: Thermal-induced, plastic-deformation-induced, and thermomechanical-induced mechanisms have been proposed by researchers; among them, the thermomechanical-induced wheel burn mechanism is more probable and convincing. Initially, the wheel burns appear as an elliptical or strip-shaped white etching layer with or without deformation on the running surface of the rails. In the latter stages of development, this may cause cracks, spalling, etc. Magnetic Flux Leakage Testing, Magnetic Barkhausen Noise Testing, Eddy Current Testing, Acoustic Emission Testing, and Infrared Thermography Testing can identify the white etching layer, and surface and near-surface cracks. Automatic Visual Testing can detect the white etching layer, surface cracks, spalling, and indentation, but cannot detect the depth of rail defects. Axle Box Acceleration Measurement can be used to detect severe wheel burn with deformation.

High-speed railway administrations are particularly concerned about safety and comfort issues, which are sometimes threatened by the differential deformation of substructures. Existing deformation-monitoring techniques are impractical for covering the whole range of a railway line at acceptable costs. Fortunately, the information about differential substructure deformation is contained in the dynamic inspection data of longitudinal level from comprehensive inspections trains. In order to detect potential differential deformations, an identification method, combining digital filtering, a convolutional neural network and infrastructure base information, is proposed. In this method, a low-pass filter is designed to remove short-waveband components of the longitudinal level. Then, a one-dimensional convolutional neural network is constructed to serve as a feature extractor from local longitudinal-level waveforms, and a binary classifier of potential differential deformations in place of the visual judgement of humans with profound expertise. Finally, the infrastructure base information is utilized to further classify the differential deformations into several types, according to the positional distribution of the substructures. The inspection data of four typical high-speed railways are selected to train and test the method. The results show that the convolutional neural network can identify differential substructure-deformations, with the precision, recall, accuracy and F1 score all exceeding 98% on the test data. In addition, four types of deformation can be further classified with the support of infrastructure base information. The proposed method can be used for directly locating adverse substructure deformations, and is also becoming a promising addition to existing deformation monitoring methods.