Search Results (64)

Point cloud intelligence integrates advanced technologies such as Light Detection and Ranging (LiDAR), photogrammetry, and Artificial Intelligence (AI) to transform transportation infrastructure management. This review highlights state-of-the-art advancements in denoising, registration, segmentation, and surface reconstruction. A detailed case study on three-dimensional (3D) mesh generation for railway fastener monitoring showcases how these techniques address challenges like noise and computational complexity while enabling precise and efficient infrastructure maintenance. By demonstrating practical applications and identifying future research directions, this work underscores the transformative potential of point cloud intelligence in supporting predictive maintenance, digital twins, and sustainable transportation systems. Full article

To address the insufficient multi-layer optimization of fastener and cushion stiffness in ballastless tracks for mixed passenger and freight railways, a vehicle–track coupled dynamic model is developed, and the effects of individual and combined stiffness parameters on track and vehicle dynamics are systematically analyzed. Based on this model, a multi-dimensional stiffness matching approach is proposed to determine appropriate stiffness ranges for mixed-use railways. Results indicate that fastener stiffness primarily affects the local dynamic response of the rail, whereas cushion stiffness has a stronger influence on overall track performance. When the damping pad stiffness exceeds 600 MPa/m, the fastener force increases sharply, posing a risk of accelerated structural deterioration. Differences in axle load and speed between passenger and freight trains induce distinct excitation patterns, leading to nonlinear variations in interlayer forces. The optimal stiffness combination is 50 kN/mm for fasteners and 600 MPa/m for damping pads under passenger conditions, and 40 kN/mm and 600 MPa/m, respectively, under freight conditions. Considering the operational requirements of mixed lines, a fastener stiffness of 40–50 kN/mm and a damping pad stiffness of 600 MPa/m are recommended. This study provides theoretical support for stiffness design and parameter optimization in ballastless tracks for mixed-use railways. Full article

Minor displacements and deflections of rail fastener clips pose a critical risk to railway safety, which are difficult to quantify accurately using traditional object detection methods. This paper proposes an improved YOLOv8-pose-based method, You Only Look Once version 8-pose with GAM, SPPF-Attention, and Wise-IoU (YOLOv8-pose-GSW) for automated and quantitative pose detection of fastener clips. Firstly, a high-precision keypoint detection network is constructed by integrating a Global Attention Mechanism (GAM) into the neck, enhancing the Spatial Pyramid Pooling Fast (SPPF) module to Spatial Pyramid Pooling Fast with Attention (SPPF-Attention) in the backbone, and adopting the Wise Intersection over Union (Wise-IoU) loss function. Subsequently, a posterior verification mechanism based on spatial constraint error is designed to eliminate unreliable detections by leveraging the inherent geometric priors of fasteners. Finally, the deflection angle, longitudinal displacement, and lateral displacement of the clip are calculated from the verified keypoints. Experimental results demonstrate that the proposed method achieves an Average Precision at IoU threshold from 0.5 to 0.95 (AP@0.5:0.95) of 77.5%, representing a 3.6% improvement over the baseline YOLOv8s-pose model, effectively balancing detection accuracy and computational efficiency. This work provides a reliable technical solution for the refined maintenance of rail fasteners. Full article

To address the challenges of improving energy balance and extending the operational lifetime of wireless sensor networks for Automated Railway Track Fastener Maintenance Robots (ARTFMR) along railways, this paper proposes an enhanced LEACH protocol incorporating Particle Swarm Optimization (PSO). Initially, network nodes are deployed, and their energy consumption is calculated to formulate a non-uniform deployment model aimed at improving energy balance, followed by network clustering. Subsequently, a routing protocol is designed, where the cluster head election mechanism integrates two critical factors—dynamic residual energy and distance to the base station—to facilitate dynamic and distributed cluster head rotation. During the communication phase, a Time Division Multiple Access (TDMA) scheduling mechanism is employed in conjunction with an inter-cluster multi-hop routing scheme. Additionally, a joint data-volume and energy optimization strategy is implemented to dynamically adjust the transmission data volume based on the residual energy of each node. Finally, simulations were conducted using MATLAB, and the results indicate that the proposed energy-balanced non-uniform deployment optimization strategy improves network energy utilization, effectively extends network lifetime, and exhibits favorable scalability. Full article

The mechanical performance of rail fasteners plays a crucial role in the track–structure interaction of high-speed railways. A reasonable lateral stiffness of the fastener system can enhance the stability and safety of train operation and prevent derailment accidents. Under seismic action, adjacent bridge spans undergo reciprocating displacement, causing the rail-fastener system near the beam ends to be subjected to lateral cyclic forces. To investigate the mechanical and deformation behavior of the WJ-8B fastener system under lateral loading, low-cycle reciprocating loading tests were conducted on the rail-fastener system considering different bolt torques. The load–displacement curves and torque–rotation curves of the fastener system were obtained, and formulas for calculating the characteristic values of the mechanical properties of the WJ-8B fastener system were fitted, which show good agreement with the experimental results. The results indicate that the lateral mechanical behavior of the WJ-8B fastener exhibits significant nonlinear characteristics, marked by three distinct inflection points in the load–displacement curve that delineate five stages: initial stage, rail shearing stage, rail sliding stage, rail contact stage, and three-point contact. The bolt torque is positively correlated with the lateral stiffness of the fastener system. Increasing the torque from 115 N·m to 190 N·m enhances the lateral bearing capacity by 29.06% in the push direction and by 38.74% in the pull direction. Meanwhile, the system torque decreases by 21.45% in the push direction and increases by 21.14% in the pull direction. Full article

Current research on the vibration characteristics of fastener clips primarily employs modal experiments combined with finite element simulations; however, limited attention has been given to the dynamic vibration behavior of clips during actual train operations. This study investigates both the quasi-static and dynamic vibration characteristics using an integrated approach of finite element simulation and dynamic testing. Based on the Vossloh W300-1 fastener system, a three-dimensional model is established. Modal and frequency response analyses, together with field test validation, reveal two significant vibration modes within 0–1000 Hz: a first-order mode at 500 Hz and a second-order mode at 560 Hz. These modes are characterized by vertical overturning of the clip arm. Dynamic testing demonstrates that the dominant frequency of the arm acceleration is strongly correlated with the second-order natural frequency, confirming that wheel–rail excitation readily triggers second-order mode resonance. The study further shows that, at train speeds of 200–350 km/h, rail corrugation with wavelengths of 99.2–173.6 mm induces high-frequency excitation at 560 Hz, resulting in resonance fatigue of the clip. As a mitigation measure, regular rail grinding is recommended to eliminate corrugation at critical wavelengths. Additionally, optimizing the clip structure to avoid resonance frequency bands is proposed. These findings elucidate the coupling mechanism between the vibration characteristics of the clip and dynamic loads, providing theoretical support for the safety evaluation of high-speed rail fastener systems and the vibration-resistant design of clips. Full article

Railway track fasteners, serving as critical connecting components, have a reliability that directly impacts railway operational safety. To address the performance bottlenecks of existing detection methods in handling complex scenarios with diverse fastener types and co-occurring multiple defects, this paper proposes a Type-Guided Expert Model-based Fastener Detection and Diagnosis framework (TGEM-FDD) based on You Only Look Once (YOLO) v8. This framework follows a “type-identification-first, defect-diagnosis-second” paradigm, decoupling the complex task: the first stage employs an enhanced YOLOv8s with Deepstar, SPPF-attention, and DySample (YOLOv8s-DSD) detector integrating Deepstar Block, Spatial Pyramid Pooling Fast with Attention (SPPF-Attention), and Dynamic Sample (DySample) modules for precise fastener localization and type identification; the second stage dynamically invokes a specialized multi-label classification “expert model” based on the identified type to achieve accurate diagnosis of multiple defects. This study constructs a multi-label fastener image dataset containing 4800 samples to support model training and validation. Experimental results demonstrate that the proposed YOLOv8s-DSD model achieves a remarkable 98.5% mean average precision at an Intersection over Union threshold of 0.5 (mAP@0.5) in the first-stage task, outperforming the original YOLOv8s baseline and several mainstream detection models. In end-to-end system performance evaluation, the TGEM-FDD framework attains a comprehensive Task mean average precision (Task mAP) of 88.1% and a macro-average F1 score for defect diagnosis of 86.5%, significantly surpassing unified single-model detection and multi-task separate-head methods. This effectively validates the superiority of the proposed approach in tackling fastener type diversity and defect multi-label complexity, offering a viable solution for fine-grained component management in complex industrial scenarios. Full article

Railway tracks at bridge approaches experience significant vertical stiffness transitions, leading to adverse effects such as settlement and increased dynamic loads, accelerating track degradation. This study explores various structural solutions, including geosynthetics, reinforced ballast, transition slabs, under sleeper pads (USPs), under ballast mats (UBMs), jet grouting, and special rail fasteners. Despite their application, these solutions often fail due to their static nature. This paper introduces an adaptive approach using special rail fastenings with real-time adjustable stiffness. This system dynamically modifies rail support characteristics based on train speed and track conditions, improving track durability, ride quality, and maintenance strategies. The findings demonstrate the potential of adaptive systems to enhance railway infrastructure performance. Full article

To mitigate and control the seismic damage risk of high-speed railway bridges and enhance their post-earthquake reparability, a prefabricated multi-layer parallel-connected slit steel plate shear damper is proposed by utilizing the energy absorption capacity of flexure–shear coupled deformation in dampers. A theoretical model for calculating the stiffness and load-bearing capacity of the proposed damper was established and validated through detailed finite element simulations. The results demonstrate that the damper exhibits stable energy dissipation efficiency under cyclic loading, along with a gradual reduction in post-yield stiffness. Subsequently, a numerical model of the high-speed railway track–bridge-damper systems (HSRTBDS) was developed, incorporating the contribution of the proposed damper to quantify its control over the seismic response of the HSRTBDS. The findings indicate that the damper effectively reduces the seismic responses of the girders, rail fasteners, and track slabs, with a maximum deformation reduction exceeding 30% in the supporting structures. However, the deformation and damage of the bridge piers slightly increased, though they remained within acceptable safety limits. The damper showed limited influence on the damage to rails, fasteners, and shear key slots. Overall, the effectiveness of the proposed damper in controlling the structural response of HSRTBD has been demonstrated and validated, providing insights for the seismic design of high-speed railway bridges in high-intensity seismic zones. Full article

Defect detection in rail fasteners constitutes a fundamental requirement for ensuring safe and reliable railway operations. Confronted with increasingly demanding inspection requirements of modern rail networks, traditional manual visual inspection methods have proven inadequate. To achieve accurate, efficient, and intelligent detection of rail fasteners, this paper presents an enhanced YOLOv5m-based defect detection model. Firstly, a dual-attention mechanism comprising Squeeze-and-Excitation and Coordinate Attention modules is employed to enhance the model. Secondly, the network architecture is redesigned by adopting MobileNetv3 as the backbone while incorporating structures with Ghost Shuffle Convolution (GSConv) modules and lightweight upsampling operators to reduce computational overhead. Finally, the original CIoU loss function in YOLOv5 is replaced with SIoU to accelerate convergence rate during training. Experimental results on a custom-built rail fastener dataset comprising 6500 images demonstrate that the enhanced model achieves 96.5% mAP and 17.9 FPS, surpassing the baseline by 3.1% and 2.1 FPS, respectively. Compared to existing detection models, this solution exhibits higher accuracy, faster inference, and lower memory consumption, providing critical technical support for edge deployment of rail fastener defect detection systems. Full article

WSNs are an important component of the Internet of Things (IoT), and the research on their routing protocols has always been a hot topic in academia. However, in ARTFMRs’ collaborative operation along railway lines, there are common problems such as energy holes, high latency, and uneven energy consumption in LWSNs. To address these issues, this paper proposes a genetic algorithm-optimized energy-aware routing protocol (GAECRPQ). Firstly, a non-uniform deployment strategy of three-line isosceles triangles is constructed to enhance coverage and balance node distribution. Secondly, an energy–distance adaptive weighting mechanism based on a genetic algorithm is introduced for cluster head (CH) selection to reduce energy consumption in hotspots and extend the network lifetime. Finally, a task-aware TDMA dynamic time slot allocation method is proposed, which incorporates the real-time task status of ARTFMRs into communication scheduling to achieve priority transmission under latency constraints. The simulation results show, that compared with six unequal clustering protocols—EADUC, EAUCA, EBUC, EEUC, LEACH, and LEACH-C—the three-line isosceles triangle deployment has a wider coverage area, and the GAECRPQ protocol increases the network lifetime by 7.4%, the lifetime by 40%, and reduces the average latency by 55.77%, 53.07%, 47.61%, 39.87%, 52.08%, and 50.48%, respectively. This verifies that GAECRPQ has good performance in terms of network lifetime and energy utilization efficiency, providing a practical solution for the collaborative operation of ARTFMRs in railway maintenance scenarios. Full article

The integrity and stability of railway fasteners are of vital importance to railway safety. To address the challenges of limited anomaly samples, irregular defect geometries, and complex operational conditions in rail fastener anomaly detection, this paper proposes an unsupervised anomaly detection method using a knowledge-distilled generative adversarial network. First, the proposed method employs collaborative teacher–student learning to model normal sample distributions, where the student network reconstructs input images as normal outputs while a discriminator identifies anomalies by comparing input and reconstructed images. Second, a multi-scale attention-coupling feature-enhancement mechanism is proposed, effectively integrating hierarchical semantic information with spatial-channel attention to achieve both precise target localization and robust background suppression in the teacher network. Third, an enhanced anomaly discriminator is designed to incorporate an enhanced pyramid upsampling module, through which fine-grained details are preserved via multi-level feature map aggregation, resulting in significantly improved sensitivity for small-sized anomaly detection. Finally, the proposed method achieved an AUC of 94.0%, an ACC of 92.5%, and an F1 score of 91.6% on the MNIST dataset, and an AUC of 94.7%, an ACC of 90.1%, and an F1 score of 87.8% on the railway fastener dataset, which proves the superior anomaly detection ability of this method. Full article

During the maintenance of railway fasteners, there are issues with the current nut disassembly and assembly operation, including low efficiency, heavy reliance on manual labor, and high physical strain. A mechanical device has been designed to move along the railway track while identifying and locating the center of the nut to perform disassembly and assembly operations. First, based on the nut disassembly and assembly process and the operating environment, the structure of the equipment was designed. This machine can simultaneously disassemble and assemble all the nuts on a single rail tie and accommodate position errors and deviations of spiral spikes. Secondly, to verify the structural reliability of the designed machine, a static simulation analysis was conducted on the key load-bearing structures under extreme operating conditions. Based on the simulation results, a lightweight design was applied to the machine’s carrier platform. The performance of the nut assembly and disassembly mechanism was optimized based on the Kriging model and the Non-dominated Sorting Genetic Algorithm (NSGA-II). The optimized machine reduced its mass by 21.7% and increased its strength by more than 30%. A transient analysis was also conducted on the optimized machine structure, further validating its strength. Finally, based on the design and optimization results, a physical prototype of the nut disassembly machine was constructed and tested. The results show that the device can efficiently perform nut disassembly and assembly tasks on the railway track. Both the mechanical structure’s reliability and functionality meet the design objectives and requirements, demonstrating significant application value for promoting the intelligent maintenance of railways. Full article

With the development of the railway industry and the progression of deep learning technology, object detection algorithms have been gradually applied to track defect detection. To address the issues of low detection efficiency and inadequate accuracy, we developed an improved orbital defect detection algorithm utilizing the YOLO11 model. First, the conventional convolutional layers in the YOLO (You Only Look Once) 11backbone network were substituted with the SPD-Conv (Spatial Pyramid Dilation Convolution) module to enhance the model’s detection performance on low-resolution images and small objects. Secondly, the EffectiveSE (Effective Squeeze-Excitation) attention mechanism was integrated into the backbone network to enhance the model’s utilization of feature information across various layers, thereby improving its feature representation capability. Finally, a small target detection head was added to the neck network to capture targets of different scales. These improvements help the model identify targets in more difficult tasks and ensure that the neural network allocates more attention to each target instance, thus improving the model’s performance and accuracy. In order to verify the effectiveness of this model in track defect detection tasks, we created a track fastener dataset and a track surface dataset and conducted experiments. The mean Average Precision (mAP@0.5) of the improved algorithm on track fastener dataset and track surface dataset reached 95.9% and 89.5%, respectively, which not only surpasses the original YOLO11 model but also outperforms other widely used object detection algorithms. Our method effectively improves the efficiency and accuracy of track defect detection. Full article

To enable accurate and efficient real-time detection of rail fastener defects under resource-constrained environments, we propose DP-YOLO, an advanced lightweight algorithm based on YOLOv5s with four key optimizations. First, we design a Depthwise Separable Convolution Stage Partial (DSP) module that integrates depthwise separable convolution with a CSP residual connection strategy, reducing model parameters while enhancing recognition accuracy. Second, we introduce a Position-Sensitive Channel Attention (PSCA) mechanism, which calculates spatial statistics (mean and standard deviation) across height and width dimensions for each channel feature map. These statistics are multiplied across corresponding dimensions to generate channel-specific weights, enabling dynamic feature recalibration. Third, the Neck network adopts a GhostC3 structure, which reduces redundancy through linear operations, further minimizing computational costs. Fourth, to improve multi-scale adaptability, we replace the standard loss function with Alpha-IoU, enhancing model robustness. Experiments on the augmented Roboflow Universe Fastener-defect-detection Dataset demonstrate DP-YOLO’s effectiveness: it achieves 87.1% detection accuracy, surpassing the original YOLOv5s by 1.3% in mAP0.5 and 2.1% in mAP0.5:0.95. Additionally, the optimized architecture reduces parameters by 1.3% and computational load by 15.19%. These results validate DP-YOLO’s practical value for resource-efficient, high-precision defect detection in railway maintenance systems. Full article