Search

Defect detection of track fasteners is a prerequisite for safe and reliable railroad operation. The traditional manual visual inspection method has been unable to meet the growing demand for railroad network inspection in China. To achieve the need for accurate, fast, and intelligent detection of rail fasteners, this paper proposes a rail fastener defect detection model based on improved YOLOv5s. Firstly, the convolutional block attention module (CBAM) is added to the Neck network of the YOLOv5s model to enhance the extraction of essential features by the model and suppress the information of minor features. Secondly, a weighted bidirectional feature pyramid network (BiFPN) is introduced to realize the multi-scale feature fusion of the model. Finally, the K-means++ algorithm is used to re-cluster the dataset to obtain the anchor box suitable for the fastener dataset and improve the positioning ability of the model. The experimental results show that the improved model achieves an average mean precision (mAP) of 97.4%, a detection speed of 27.3 FPS, and a model memory occupancy of 15.5 M. Compared with the existing target detection model, the improved model has the advantages of high detection accuracy, fast detection speed, and small model memory occupation, which can provide technical support for edge deployment of rail fastener defect detection. [ABSTRACT FROM AUTHOR]

A type of short-pitch rail corrugation on tracks with resilient fasteners called 'Vanguard' fasteners is widely found on different metro lines in China. Field measurements and numerical calculations are used to investigate the cause of the corrugation formation. The corrugation with wavelengths of 30–60 mm occurs on both the curved and straight tracks through field measurements. Corrugation passing frequencies are in the range of 340–700 Hz. The frequencies of pinned-pinned resonance, the P2 resonance and the wheelset torsional resonance are far away from the corrugation passing frequencies. The investigated results show the vertical dynamic characteristic of the track is the main factor for occurrence of the short-pitch rail corrugation. The short-pitch rail corrugation arises by a wavelength-fixing mechanism of rail vertical bouncing on the resilient fasteners. The corrugation wavelength mainly is related to the vertical stiffness of the rail fasteners. Rail corrugation with wavelengths of 25–63 mm easily occurs on tracks with resilient fasteners with the vertical stiffness of less than 20 MN/m. [ABSTRACT FROM AUTHOR]

In this paper, the Vossloh W300 fastener currently used in China's high-speed railway is taken as the research object. Combined with the requirements of ISO 10846 series standards and the knowledge of the author, to the best of the authors knowledge, the first high-frequency dynamic stiffness test system for elastic elements of rail fastener has been successfully developed and built. By taking the influence of preloading into consideration, the vertical and lateral dynamic frequency-dependent stiffness and loss factor of high-speed railway W300 fastener at 5~1250 Hz were obtained through the 1:1 test model coupling test method for the first time. Referring to the prediction method of high-speed railway wheel-rail vibration and noise, and factoring in the high-speed rotating, the infinite length of plate track structure and high-speed movement of sound source to wheels, the Kelvin - Voigt simulation sub-model of fastening system was added to predict and analyze the difference between the total sound pressure levels of the wheel and rail that is relative to the standard point under the constant stiffness and frequency-dependent stiffness of fastener. The results indicate that the frequency-dependent characteristics of fastener stiffness have significant impact on the spectrum of wheel-rail interaction forces, total sound pressure levels of rail and wheel-rail. The total sound pressure level of the wheel-rail relative to standard point, which factors in the frequency-dependent stiffness of fastener was approximately reduced 2.6 dB, that is, the frequency-dependent stiffness of fastener can rectify the overestimation of wheel-rail sound pressure level by stiffness coefficient. [ABSTRACT FROM AUTHOR]

Brace sleeve (BS) fasteners, i.e., nut and bolt, are small components but play essential roles in fixing BS and cantilever in railway catenary system. They are commonly inspected by onboard cameras using computer vision to ensure the safety of railway operation. However, most BS fasteners cannot be directly localized because they are too small in the inspection images. Instead, the BS is first localized for detecting the BS fastener. This leads to a new problem that the localized BS boxes may not contain the complete BS fasteners due to low localization accuracy, making it infeasible to further diagnose the fastener conditions. To tackle this problem, this article proposes a novel pipeline for BS fastener looseness diagnosis. First, the competitive deep learning model Faster RCNN ResNet101 is used to coarsely localize BSs. Second, an action-driven reinforcement learning agent is adopted to refine the coarse-localized boxes through a dynamic position searching process. Then, BS fasteners are extracted from the refined localized BS image by the deep segmentation model YOLACT++, which is fast and interpretable. Finally, a looseness diagnosis criterion based on segmented information are proposed. We evaluate the performance of submodels independently and the overall performance of the whole model on a real-life catenary image dataset collected from a high-speed line in China. The test results show that the proposed method is effective for BS looseness detection in railway catenary. [ABSTRACT FROM AUTHOR]

The article offers information about the investigation issued by the European Union on the compliance dispute regarding the legitimacy of anti-dumping measures over imported Chinese fasteners. It mentions that risk of price manipulation in non-market economies,24 the method chosen by the European Union Commission.