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The fracture phenomenon of fastener clips occurs frequently in the sections with serious rail corrugation of high-speed railway, which brings huge potential safety hazards to track service and driving safety. In order to prevent the vibration fatigue fracture of fastener clips in high-speed railway, a dynamic vibration absorber (DVA) for clips is proposed in this paper to suppress the primary sensitive frequency of the corresponding resonant of clips. Firstly, based on the optimal design theory of DVA, a parameters design method of ring-shaped DVA suitable for clips is proposed. Secondly, the vibration characteristics of the high-speed railway fastener system are investigated, and the main reason for the fastener clips fracture is analyzed by simulation analysis combined with filed test. Then, focusing on the primary resonant frequency of clips, the vibration reduction effect of the ring-shaped DVA is analyzed. The results show that the ring-shaped DVA can change the natural frequency of the clip system, reduce the vibration response in the range of the original resonant frequency, and avoid the resonance phenomenon. Finally, the influences of mass ratio, frequency ratio, and damping ratio on the vibration reduction effect are analyzed, and the effectiveness of the parameters design method of ring-shaped DVA proposed in this paper is further proved. The research results can provide methodological support for the anti-breakage design of high-speed railway fastener clips. [ABSTRACT FROM AUTHOR]

The paper presents comparative laboratory investigations of tension fastener models that attach rails to concrete sleepers. The aim of the paper was to assess the influence of geometric nonconformity of the actual industrial product that meets the PKP Polskie Linie Kolejowe S.A. requirements on the operation of a tension-fastening clamp under stress. Due to the cost and limited possibility of research of the actual industrial product, an additional objective of the research was to validate the usefulness of it in the comparative assessment of the models. In the research, the authors used models of tension fastening clamps manufactured in incremental engineering technology (3D print) on scale 1:2. The properties of the fastening clamps (in their nominal shape) were compared (described in the PKP Polskie Linie Kolejowe S.A. documentation) with the fastening clamps of the actual shape. In the investigation, the authors have confirmed the negative influence of the non-conformance of the shape of the actual fastening clamps with the nominal ones. [ABSTRACT FROM AUTHOR]

With the increase of the running time of the subway system, long-term use will lead to the reduction of damping, the change of the operating state of the wheels and the track, and the effect of vibration damping measures will gradually weaken. In order to maintain a low level of environmental vibration in vibration sensitive areas, the reconstruction of some existing lines with high level vibration is imminent. In order to solve this problem, this paper focuses on the reconstruction of the existing line of longitudinal sleeper track, which is commonly used in the middle and high level vibration reduction measure in the subway. Because of the high cost of reconstruction of the slab and its lower part, this paper tries to realize the reconstruction of the existing longitudinal sleeper track by changing the fastener system. In this paper, the environmental vibration prediction model of longitudinal sleeper track is established by using the theory of infinite long period structure and the 2.5-dimensional finite element and boundary element (2.5D FE-BE) environmental vibration prediction model. The validity of the numerical model is determined by comparing the field experiment with the numerical simulation. On the basis of the above, the stiffness matching of longitudinal sleeper and fastener is explored by using the verified numerical model. The reasons for the poor vibration reduction effect of the existing longitudinal sleeper matching floating rail fastener (low stiffness) are analyzed. And the matching strategy of the supporting stiffness for vibration reduction of longitudinal sleeper and fastener are summarized. [ABSTRACT FROM AUTHOR]

A rod fastened rotor (RFR), which contains serial disks, rods (or a rod), and a torque tube, is the main form of heavy duty gas turbines and light duty aero engines. The roughness of the interface is the main parameter affecting the bending stiffness of RFR, and the jumpiness of the disks is the intuitive representation of the unbalance of the rotor. In this paper, a multi-optimization algorithm, NSGA-Ⅱ, was used to improve the vibration behavior of the RFR-bearing system. First, the quantitative relation between the bending stiffness of the rotor and the roughness of the interface was set up by combining the Persson contact theory and the Greenwood–Williamson model, and the nondimensional bending stiffness (NS) was proposed as the optimization objective of roughness. Then, the optimization objective of the jumpiness of the disk, (A + B), was rendered based on the minimum unbalance force. Finally, the multi-optimization on the roughness and jumpiness of the RFR was carried out, and the vibration behavior was improved greatly. In addition, this multi-optimization method was applied to a practical RFR, and the experimental results showed that the vibration amplitude was reduced by about 82%. The work in this paper can provide the guidance for vibration reduction of the rotor system. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) inspection has gradually replaced manual inspection of transmission tower, which produces many images. While it is laborious and time-consuming to manually analyze these images, there are also challenges in automatically detecting small-size defective components such as bolts in transmission tower images, due to problems including complex background, small size, and many similar objects of bolts. In this paper, by virtue of multi-scale features and context information, we propose a deep neural network named Camp-Net (Context Information and Multi-Scale Pyramid Network) to identify bolts defect in transmission tower images. First, multi-scale feature fusion combines deep features and shallow features in convolutional networks to detect small-size bolts. Second, context information fusion puts the information around bolts into the detection network to remove the disturbance of complex background and similar objects. An image dataset containing defective bolts and normal bolts is constructed for model training and testing. Experimental results show that bolts with loose pins and bolts without pins among fittings in transmission tower can be accurately identified with the proposed model. The Average Precision (AP) of defective bolts detection of this model can be 11.4% higher than that of the commonly used high performance model, Faster R-CNN. [ABSTRACT FROM AUTHOR]

The vibration and transmission characteristics of track components during vehicle operation is a complex issue. To address this issue, this paper proposes a flexible wheelset-damped track-tunnel-soil model that considers the interaction between the flexible wheelset and the rail. Using field measurements and the developed model, this study investigates the vibration characteristics of track components and explores the influence of fastener stiffness and damping pad stiffness on track vibration and vibration transfer characteristics between components from an energy perspective, using track spectrum power flow as the evaluation index. The results demonstrate that the model considering wheel-rail coupling can more comprehensively reflect the vibration characteristics of the track during train operation. Compared to fasteners with lower stiffness, damping pads with lower stiffness can reduce track vibration in a wider frequency range. Lower fastener stiffness reduces the transmission of vibration in 50∼2000 Hz, while lower damping pad stiffness reduces the transmission of vibrations in 1∼2000 Hz. [ABSTRACT FROM AUTHOR]

Although bolted joints may appear simple and are easy to manipulate, they are challenging to model and analyze due to their complex structural patterns and statically indeterminate nature. Ensuring the structural integrity of these joints requires maintaining proper bolt preload and clamping force, which is crucial for preventing failures such as overload, excessive bearing stress, fatigue, and stripping caused by seizing or galling. Achieving the necessary clamping force involves carefully controlling the input tightening torque, which is divided into the pitch torque and the friction torques at the bolt or nut bearing surfaces and in the engaged threads. The resulting clamping force is critical for generating the required force within the bolt. However, the achieved bolt force depends on several factors, such as friction at the joint's contact surfaces, grip length, and the relative rotation between the bolt and nut during tightening. Friction at the contact surfaces, particularly beneath the bolt head or nut and between the threads, consumes a significant portion of the applied tightening torque—approximately 90%. This paper explores the three existing bolt internal pitch, bearing, and thread friction torques that are generated by the external applied torque in a bolted joint, as well as their contributions and variations throughout a loading cycle composed of three phases: tightening, settling, and untightening. An analytical model is developed to determine these torque components, and its results are compared with those obtained from finite element (FE) modeling and experimental testing from previous studies. Finally, this study examines the torque–tension relationship during bolt tightening, offering insights into the required accuracy of bolt and clamped member stiffness. The bolt samples used in this study include M12 × 1.75 and M36 × 4 hex bolts. [ABSTRACT FROM AUTHOR]

Short pitch corrugation has been a problem for railways worldwide over one century. In this paper, a parametric investigation of fastenings is conducted to understand the corrugation formation mechanism and gain insights into corrugation mitigation. A three-dimensional finite element vehicle–track dynamic interaction model is employed, which considers the coupling between the structural dynamics and the contact mechanics, while the damage mechanism is assumed to be differential wear. Various fastening models with different configurations, boundary conditions, and parameters of stiffness and damping are built up and analysed. These models may represent different service stages of fastenings in the field. Besides, the effect of train speeds on corrugation features is studied. The results indicate: (1) Fastening parameters and modelling play an important role in corrugation formation. (2) The fastening longitudinal constraint to the rail is the major factor that determines the corrugation formation. The fastening vertical and lateral constraints influence corrugation features in terms of spatial distribution and wavelength components. (3) The strengthening of fastening constraints in the longitudinal dimension helps to mitigate corrugation. Meanwhile, the inner fastening constraint in the lateral direction is necessary for corrugation alleviation. (4) The increase in fastening longitudinal stiffness and damping can reduce the vibration amplitudes of longitudinal compression modes and thus reduce the track corrugation propensity. The simulation in this work can well explain the field corrugation in terms of the occurrence possibility and major wavelength components. It can also explain the field data with respect to the small variation between the corrugation wavelength and train speed, which is caused by frequency selection and jump between rail longitudinal compression modes. [ABSTRACT FROM AUTHOR]

Many compartments are prone to pose safety hazards such as loose fasteners or object intrusion due to their confined space, making manual inspection challenging. To address the challenges of complex inspection environments, diverse target categories, and variable scales in confined compartments, this paper proposes a novel GMS-YOLO network, based on the improved YOLOv8 framework. In addition to the lightweight design, this network accurately detects targets by leveraging more precise high-level and low-level feature representations obtained from GhostHGNetv2, which enhances feature-extraction capabilities. To handle the issue of complex environments, the backbone employs GhostHGNetv2 to capture more accurate high-level and low-level feature representations, facilitating better distinction between background and targets. In addition, this network significantly reduces both network parameter size and computational complexity. To address the issue of varying target scales, the first layer of the feature fusion module introduces Multi-Scale Convolutional Attention (MSCA) to capture multi-scale contextual information and guide the feature fusion process. A new lightweight detection head, Shared Convolutional Detection Head (SCDH), is designed to enable the model to achieve higher accuracy while being lighter. To evaluate the performance of this algorithm, a dataset for object detection in this scenario was constructed. The experiment results indicate that compared to the original model, the parameter number of the improved model decreased by 37.8%, the GFLOPs decreased by 27.7%, and the average accuracy increased from 82.7% to 85.0%. This validates the accuracy and applicability of the proposed GMS-YOLO network. [ABSTRACT FROM AUTHOR]

The sustainability of the manufacturing industry is of special importance to increase the protection of the environment. The production of fasteners like self-piercing rivets, however, is costly, time-consuming and energy-intensive. The heat treatment and the coating, which are mandatory in conventional self-piercing rivets to achieve adequate strength, ductility and corrosion resistance, are especially crucial in this respect. Within this paper, an approach for an increase in the sustainability in fastener production is presented. The use of alternative, high strain hardening stainless steels as rivet material enables a shortening of the process chain, because post treatment of the rivets after they are formed can be omitted. As the change in rivet material and processing causes some issues along the process chain, the focus of this paper is on the holistic evaluation of the challenges within the forming of high strain hardening steel and the impact of the changed rivet properties on the joining result. [ABSTRACT FROM AUTHOR]

Rails play a vital role in the bearing and guidance of high-speed trains, and the normal condition of rail components is the guarantee of the operation and maintenance safety. Fasteners are critical components for fixing the rails, so it is particularly important to detect whether they are in a normal state or not. The current rail-fastener detection models have some drawbacks, including poor generalization ability, large model volume and low detection efficiency. In view of this, an improved YoLoX-Nano rail-fastener-defect-detection method is proposed in this paper. The CA attention mechanism is added to the three output feature maps of CSPDarknet and the enhanced feature extraction part of the Path Aggregation Feature Pyramid Network (PAFPN); the Adaptively Spatial Feature Fusion (ASFF) is added after the PAFPN output feature map, which enables the semantic information of the high-level features and the fine-grained features of the bottom layer to be further enhanced. The improved YoLoX-Nano model has improved the AP value by 27.42% on fractured fasteners, 15.88% on displacement fasteners and 12.96% on normal fasteners. Moreover, the mAP value is improved by 18.75%, and it is 14.75% higher than the two-stage model Faster-RCNN on mAP. In addition, compared with YoLov7-tiny, the improved YoLoX-Nano model achieves 13.56% improvement on mAP. Although the improved model increases a certain amount of calculation, the detection speed of the improved model has been increased by 30.54 fps and by 32.33 fps when compared with that of the Single-Shot Multi-Box Detector (SSD) model and the You Only Look Once v3 (YoLov3) model, reaching 54.35 fps. The improved YoLoX-Nano model enables accurate and rapid identification of the defects of rail fasteners, which can meet the needs of real-time detection. Furthermore, it has advantages in lightweight deployment of terminals for rail-fastener detection, thus providing some reference for image recognition and detection in other fields. [ABSTRACT FROM AUTHOR]

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Rail-fastening components are essential for ensuring the safety of urban rail systems by securing rails to sleepers. Traditional inspection methods rely heavily on manual labor and are inefficient. This paper introduces a novel approach to address these inefficiencies and the challenges faced by computer vision-based inspections, such as missed detections due to imbalanced samples and limitations in conventional image segmentation techniques. Our approach transitions the industry's focus from qualitative to a more precise quantitative analysis of rail-fastening components. We propose Mask-FRCN, an advanced image segmentation network that incorporates three key technological enhancements: the fully refined convolutional network module (FRCN),which refines the segmentation boundaries for SFC-type fasteners; the Channel-WiseKnowledge Distillation (CWD) algorithm, which boosts the model's inference efficiency; and the FCRM methodology, which enhances the extraction capabilities for features specific to SFC-type fasteners. Furthermore, we introduce a fastener system inspection and quantization method based on the Mask FRCN method (FIQ), a novel technique for quantifying the condition of components by using image features, template matching with random forests, and a clustering calculation method derived from segmentation results. Experimental results validate that our method significantly surpasses existing techniques in accuracy, thereby offering a more efficient solution for inspecting rail-fastening components. The enhanced Mask-FRCN achieves a segmentation accuracy of 96.01% and a reduced network size of 36.1 M. Additionally, the FIQ method improves fault detection accuracy for SFC-type fasteners to 95.13%, demonstrating the efficacy and efficiency of our innovative approach. [ABSTRACT FROM AUTHOR]

Fastener screws are critical components of rail fasteners. For the fastener screw maintenance robot, an image-based fast fastener screw detection method is urgently needed. In this paper, we propose a light-weight model named FSS-YOLO based on YOLOv5n for rail fastener screw detection. The C3Fast module is presented to replace the C3 module in the backbone and neck to reduce Params and FLOPs. Then, the SIoU loss is introduced to enhance the convergence speed and recognition accuracy. Finally, for the enhancement of the screw detail feature fusion, the shuffle attention (SA) is incorporated into the bottom-up process in the neck part. Experiment results concerning CIoU and DIoU for loss, MobileNetv3 and GhostNet for light-weight improvement, simple attention mechanism (SimAM), and squeeze-and-excitation (SE) attention for the attention module, and YOLO series methods for performance comparison are listed, demonstrating that the proposed FSS-YOLO significantly improves the performance, with higher accuracy and lower computation cost. It is demonstrated that the FSS-YOLO is 7.3% faster than the baseline model in FPS, 17.4% and 19.5% lower in Params and FLOPs, respectively, and the P, mAP@50, Recall, and F1 scores are increased by 10.6% and 6.4, 13.4%, and 12.2%, respectively. [ABSTRACT FROM AUTHOR]

Changes in the geological environment and track wear, and deterioration of train bogies may lead to the looseness of subway fasteners. Identifying loose fasteners randomly distributed along the subway line is of great significance to avoid train derailment. This paper presents a convolutional autoencoder (CAE) network-based method for identifying fastener loosening features from the distributed vibration responses of track beds detected by an ultra-weak fiber Bragg grating sensing array. For an actual subway tunnel monitoring system, a field experiment used to collect the samples of fastener looseness was designed and implemented, where a crowbar was used to loosen or tighten three pairs of fasteners symmetrical on both sides of the track within the common track bed area and the moving load of a rail inspection vehicle was employed to generate 12 groups of distributed vibration signals of the track bed. The original vibration signals obtained from the on-site test were converted into two-dimensional images through the pseudo-Hilbert scan to facilitate the proposed two-stage CAE network with acceptable capabilities in feature extraction and recognition. The performance of the proposed methodology was quantified by accuracy, precision, recall, and F1-score, and displayed intuitively by t-distributed stochastic neighbor embedding (t-SNE). The raster scan and the Hilbert scan were selected to compare with the pseudo-Hilbert scan under a similar CAE network architecture. The identification performance results represented by the four quantification indicators (accuracy, precision, recall, and F1-score) based on the scan strategy in this paper were at least 23.8%, 9.5%, 20.0%, and 21.1% higher than those of the two common scan methods. As well as that, the clustering visualization by t-SNE further verified that the proposed approach had a stronger ability in distinguishing the feature of fastener looseness. [ABSTRACT FROM AUTHOR]

Cold-formed steel (CFS) elements have gained significant attention in the field of structural engineering due to their numerous advantages, including high strength-to-weight ratio, cost-effectiveness, and ease of assembly and prefabrication. This review paper presents a comprehensive state-of-the-art analysis of the design and analysis of CFS structures, with a specific focus on columns and beams. The primary objectives and aims of this review paper are to provide a detailed assessment of the factors influencing the behavior and performance of CFS elements, including partial composite action, fastener spacing, bolt arrangement, web aperture, stiffeners, and connection spacing, to propose and present various formulas and methodologies that accurately estimate critical buckling loads, strength, and moment resistance for CFS members, and to emphasize the significance of proper screw and bolt placement in preventing premature failure and enhancing the overall load-carrying capacity of CFS structures. Additionally, the impact of temperature on the mechanical properties and performance of CFS members is discussed. The review paper proposes different formulas and methodologies to accurately estimate critical buckling loads, strength, and moment resistance for CFS members. Moreover, the paper highlights the importance of proper screw and bolt placement to prevent early failure and improve the overall load-carrying capacity of CFS structures. The discussion also emphasizes the need for revisions in existing standards and codes to provide more practical guidelines for designers and engineers. Overall, this state-of-the-art review paper provides valuable insights and recommendations for researchers and practitioners involved in the design and analysis of CFS elements. [ABSTRACT FROM AUTHOR]

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]

Modeling of the structural behavior of the housing of an electromagnetic railgun is usually carried out without considering the electromagnetic origin of the forces; the magnetic pressure acting on the rails is assumed to act at the rail surfaces only. This approximation proves to be a great challenge, as moving pressure profiles at speeds of a railgun projectile are very rare in purely mechanical investigations. In the past, we have published several papers on the structural mechanics of railgun housings characterized by discrete supports. We could show that the displacement of rail surfaces at the position of the armature can reach amplitudes that are not negligible. In fact, in the case of metal brush armatures, the increase in distance between the rail surfaces of only tenths of a millimeter can make the difference between the solid contact and arcing to increase. We also matched our findings to experimental data that were, however, so far only gained in static tests. In this paper, we analyze the foundations of our mechanical modeling using electromagnetic calculations. We calculate a 3-D distribution of the $J \times B$ volume force density in the rails and use them as input for our structural mechanics model. A second point to be addressed concerns the parts made from conducting materials, such as our discrete supports made from steel bolts, which are situated in rapidly changing magnetic fields during the experiments. Again, the influence on our structural mechanics modeling is discussed. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of a magnetic lead screw (MLS) equipped with skewed arc magnets. Skewed arc magnets are adopted to approximate the helical ones, because the latter is hard to realize. Although similar method had been proposed in the previous literature, discussion about the parameters of the skewed arc magnets was insufficient and little attention was paid to the gear ratio of the resultant MLS. In this paper, the principle of approximation by skewed arc magnets is proposed and discussed, and the performance of the MLS with skewed arc magnets is evaluated and compared with the ideal MLS in terms of thrust force, torque, and gear ratio. It is highlighted that the gear ratio is the fundamental indicator of the MLS’s performance, besides the thrust force and torque. It is shown that the gear ratio of MLS is mainly affected by the reference angle and the arc angle of skewed arc magnets, and optimal values for the two angles are obtained. In addition, the effects of the magnet magnetization on thrust force and torque are analyzed. Finally, dovetail budge and groove are introduced between the adjacent magnet segments for simpler fabrication. [ABSTRACT FROM AUTHOR]

Threaded fasteners are vastly used in the industry due to ease of mounting and dismounting and flexibility of design. Nonetheless, several researchers indicate that most failures recorded on nearly any kind of machinery and vehicles are initiated at fasteners. This review paper aims at summarising the failures involving threaded fasteners available in the literature. The analysis involves categorization of the failures according to the root cause, e.g., incorrect assembly, overload, fatigue, preload loss during operation, among others. The purpose of this review paper is to give a systematic glance of the topic to the scientific and technical community, to correctly orient future investigations on the broad topic of threaded fasteners. [ABSTRACT FROM AUTHOR]

This paper introduces a batch fabrication method to manufacture Micro-Electro-Mechanical System (MEMS) platforms for 3D integration of sensors spatially-distributed on a 2D plane. In the heart of the concept is a foldable MEMS structure with polymer hinges which is used to support and guide the assembly of the discrete planar sensors by means of folding them into a 3D shape, like origami, providing controlled distribution of sensors in space. Flexible hinges carrying the electrical interconnects are a critical structural element of the platform and material selection study for those is the main focus of this paper. We analyzed different materials for flexible hinges fabrication, including photo-definable polyimide and parylene-C. Three approaches for sensor integration are presented: 1) co-fabrication; 2) sensor drop-in; and 3) transfer bonding. The prototypes of structures with different flexible hinge materials were fabricated and evaluated based on their mechanical flexibility, chemical compatibility, and material outgassing. Parylene-C exhibited similar or better performances compared to polyimide, demonstrating in each of the experiments a higher degree of thermal flexibility up to 350 °C, a superior chemical resistance against hydrofluoric acid, and 2.9 times lower outgassing. [2020-0301] [ABSTRACT FROM AUTHOR]

This paper presents new rotational and longitudinal symmetric self-clinching fasteners to fabricate reliable connections in busbars with low electrical resistance for energy distribution systems. Connections consist of form-closed joints that are hidden inside regions where two busbars overlap. The investigation into the fabrication and performance of the new self-clinched joints involved finite element modelling and experimentation to determine the required forces and to evaluate the electric current flow and the electrical resistance at different service temperatures. The original design of the joints that was proposed in a previous work was modified to account for busbar strips of copper and/or aluminum with similar or dissimilar thicknesses, connected by means of self-clinching fasteners made from the same materials of the busbars, instead of steel. The effectiveness of the new self-clinched joints was compared to that of conventional bolted joints that are included in the paper for reference purposes. The results show that rotational symmetric self-clinching fasteners yield lighter fabrication and more compact joints with a similar electrical resistance to that of bolted joints. They also show that longitudinal symmetric self-clinching fasteners aimed at replicating the resistance-seam-welding contact conditions yield a reduction in electrical resistance to values close to that of ideal joints, consisting of two strips in perfect contact and without contaminant or oxide films along their overlapped surfaces. [ABSTRACT FROM AUTHOR]

As an important part of track inspection, the detection of rail fasteners is of great significance to improve the safety of train operation. Additionally, rail fastener detection belongs to small-target detection. The YOLOv4 algorithm is relatively fast in detection and has some advantages in small-target detection. Therefore, YOLOv4 is used for rail fastener status detection. However, YOLOv4 still suffers from the following two problems in rail fastener status detection. First, the features extracted by the original feature extraction network of YOLOv4 are relatively rough, which is not conducive to crack anomaly detection on rail fasteners. In addition, the traditional convolutional neural network has a larger number of parameters and calculations, which are difficult to run on the embedded system with low memory and processing power. To effectively solve those two problems, this paper proposes a rail fastener status detection algorithm based on MobileNet-YOLOv4 (M-YOLOv4). The edge features and texture features of rail fasteners are very important for rail fastener detection, and CSPDarknet53 cannot effectively extract the features of fasteners. The MobileNet is used to replace the CSPDarknet53 feature extraction network in the YOLOv4 algorithm, which can extract subtle features of rail fasteners and reduce the number of parameters and calculations of the algorithm. The experimental results show that the M-YOLOv4 algorithm has high detection accuracy and low resource consumption in rail fastener status detection. The false-alarm rate (FAR), missed-alarm rate (MAR), and error rate (ER) were 5.71%, 1.67%, and 4.24%, respectively, and the detection speed reached 59.8 fps. Compared with YOLOv4, the number of parameters and calculations were reduced by about 80.75% and 83.20%, respectively. [ABSTRACT FROM AUTHOR]

Hyperspectral imagery (HSI) has shown promising results in real-world applications. However, the technological evolution of optical sensors poses two main challenges in HSI classification: 1) the spectral band is usually redundant and noisy and 2) HSI with millions of pixels has become increasingly common in real-world applications. Motivated by the recent success of hybrid huberized support vector machines (HHSVMs), which inherit the benefits of both lasso and ridge regression, this paper first investigates the advantages of HHSVM for HSI applications. Unfortunately, the existing HHSVM solvers suffer from prohibitive computational costs on large-scale data sets. To solve this problem, this paper proposes simple and effective stochastic HHSVM algorithms for HSI classification. In the stochastic settings, we show that with a probability of at least $1-\varrho $ , our algorithms find an $\epsilon $ -accurate solution using $\tilde {O}({1}/{\lambda _{2}\epsilon })$ iterations. Since the convergence rate of our algorithms does not depend on the size of the training set, our algorithms are suitable for handling large-scale problems. We demonstrate the superiority of our algorithms by conducting experiments on large-scale binary and multiclass classification problems, comparing to the state-of-the-art HHSVM solvers. Finally, we apply our algorithms to real HSI classification and achieve promising results. [ABSTRACT FROM AUTHOR]

This paper deals with equivalent 2-D finite-element (FE) models of permanent magnet lead screw (PMLS), to accurately predict the thrust force. Due to the helical-shape permanent magnets (PMs), the magnetic fields of PMLS are typically 3-D and non-symmetric. However, the PMLS is approximately symmetric in some cases and an equivalent 2-D axis-symmetric FE model is developed instead of 3-D models. The problem lies in that no evaluation about the accuracy and applicability of the 2-D axis-symmetric FE model was offered in previous literature. In this paper, the 2-D axis-symmetric FE model is derived based on the concept of equivalent current sheets of PMs. The limitations of the existing 2-D axis-symmetric FE model are clarified, and a novel equivalent 2-D torque FE model is then proposed to predict thrust force with the gear ratio when the 2-D axis-symmetric FE model is not appropriate. It is shown that the choice of equivalent 2-D FE model depends on the lead angle of PMLS, 2-D axis-symmetric FE model is applicable in the range of (0°, 15°), and 2-D torque FE model is preferred in the range of (65°, 90°). [ABSTRACT FROM AUTHOR]

With the remarkable recent progress on learning deep generative models, it becomes increasingly interesting to develop models for controllable image synthesis from reconfigurable structured inputs. This paper focuses on a recently emerged task, layout-to-image, whose goal is to learn generative models for synthesizing photo-realistic images from a spatial layout (i.e., object bounding boxes configured in an image lattice) and its style codes (i.e., structural and appearance variations encoded by latent vectors). This paper first proposes an intuitive paradigm for the task, layout-to-mask-to-image, which learns to unfold object masks in a weakly-supervised way based on an input layout and object style codes. The layout-to-mask component deeply interacts with layers in the generator network to bridge the gap between an input layout and synthesized images. Then, this paper presents a method built on Generative Adversarial Networks (GANs) for the proposed layout-to-mask-to-image synthesis with layout and style control at both image and object levels. The controllability is realized by a proposed novel Instance-Sensitive and Layout-Aware Normalization (ISLA-Norm) scheme. A layout semi-supervised version of the proposed method is further developed without sacrificing performance. In experiments, the proposed method is tested in the COCO-Stuff dataset and the Visual Genome dataset with state-of-the-art performance obtained. [ABSTRACT FROM AUTHOR]

For global template matching (GTM), which is commonly used in the positioning of rail fasteners, only the fastener template is used to search the global image in both two dimensions, which will result in errors in two dimensions, and the lower positioning accuracy will be caused. A positioning method for rail fasteners based on double template matching (DTM) is proposed in this paper, in which the double template contains the rail template and the fastener template. First, the rail template is used to scan the original image in horizontal dimension, and the squared Euclidean distance (SED) is used to obtain the rail positioning in the original image. Combining with the prior knowledge of the fastener template image, the image composed of the rail and the fastener can be obtained, which is called the Rail Area Map (RAM) in this paper. Then, after preprocessing the RAM and the fastener template image, the fastener template image is used to scan the RAM in vertical dimension, and the normalized correlation coefficient (NCC) is used to calculate the similarity between the template and the subgraph of the RAM to achieve precise positioning of the fastener. The proposed DTM method adopts a positioning strategy from coarse to fine, and two templates are used to complete different positioning tasks in their own dimension, respectively. Due to the rail can be precise positioned in horizontal dimension, the error of the fastener positioning in the horizontal dimension can be avoided, and thus, the positioning accuracy can be improved. Experiments on the on-site line fastener images prove that the proposed method can effectively achieve the precise positioning of fasteners. [ABSTRACT FROM AUTHOR]

The catenary support device inspection is of crucial importance for ensuring safety and reliability of railway systems. At present, visual detection tasks of catenary support devices defect are performed by trained personnel based on the images taken periodically by industrial cameras installed on inspection vehicle in a limited period of time at midnight. However, the inspection mean is inappropriate for low efficiency and high cost. This paper presents a novel network based on unmanned aerial vehicle (UAV) images for catenary support device inspection and focuses on small object detection and the imbalanced dataset. With regards to the first aspect, based on a pyramid network structure, the improved Faster R-CNN consists of a top-down-top feature pyramid fusion structure, which heavily fuses high-level semantic information and low-level detail information. The feature map fusions of three different pooling scales are employed for improving detection accuracy of predicted bounding boxes. With regards to the second, we copy and paste the small proportion objects of dataset for avoiding category imbalance. Finally, quantitative and qualitative evaluations illustrate that the improved Faster-RCNN achieves better performance over the classic methods, yet remains convenient and efficient. [ABSTRACT FROM AUTHOR]

Fasteners are an important part of railway security. The application of computer vision and image processing on fastener detection has become a research hot spot. However, most of the researches have focused on the missing and damage detection of fasteners up to now. In this paper, a method based on image processing is proposed to locate the center of the hexagon nut in the railway fastener. The precise position of the hexagon nut can be used for the automatic replacement of the nut, which is of great significance for the automatic railway construction and maintenance. The locating method proposed in this paper can be divided into two steps. First, the improved Canny detector which combines surrounding suppression and iterative excitation is used to extract the contours of the fastener. Then, a multifeature hierarchical locating (MHL) algorithm is proposed, which integrates the circle contour of the gasket and the hexagon features of the nut. The experimental results show that the improved Canny detector suppresses noises effectively and highlights the contours of fastener. The MHL algorithm in the next step improves the locating accuracy remarkably while guaranteeing the robustness meantime. [ABSTRACT FROM AUTHOR]

The objective of this paper is to advance the state of the art of aggressive space mapping (ASM) by demonstrating how, for resonant structures, and microwave filters in particular, an ASM-based optimization procedure may converge to the desired target performance in just one step. This behavior is first justified in physical terms, using the electromagnetic cavity perturbation theory, and is then investigated numerically. Several practical filter design demonstrations are also presented. This paper is concluded with a conceptual definition of Space Alignment in the context of ASM. [ABSTRACT FROM AUTHOR]

The aim of this paper is to gain insight into the nonlinear vibration feature of a dynamic model of a gas turbine. First, a rod fastening rotor-bearing coupling model with fixed-point rubbing is proposed, where the fractal theory and the finite element method are utilized. For contact analysis, a novel contact force model is introduced in this paper. Meanwhile, the Coulomb model is adopted to expound the friction characteristics. Second, the governing equations of motion of the rotor system are numerically solved, and the nonlinear dynamic characteristics are analyzed in terms of the bifurcation diagram, Poincaré map, and time history. Third, the potential effects provided by contact degree of joint interface, distribution position, and amount of contact layer are discussed in detail. Finally, the contrast analysis between the integral rotor and the rod fastening rotor is conducted under the condition of fixed-point rubbing. [ABSTRACT FROM AUTHOR]

This paper introduces a methodology to identify missing fasteners in railroad tracks using laser Doppler vibrometer (LDV) measurements recorded from a moving rail car. In order to investigate the effects of missing fasteners on the rail's dynamic response, three approaches are adopted: (i) rail vibration tests, (ii) modal analysis, and (iii) simulated moving measurements. Next, a missing fastener detection algorithm was introduced based on the frequency range of interest and the feature identified in those approaches. The proposed methodology is validated through field tests conducted on the railway track at the Transportation Technology Center (TTC) in Pueblo, CO, with two different speeds at 32 km/h (20 mph) and 48 km/h (30 mph). [ABSTRACT FROM AUTHOR]

During the operation of drilling rigs, bolts are subjected to significant loads, rendering bolt groups vulnerable to failure due to uneven load distribution. This study investigates the multi-bolt load distribution characteristics of eight-gear and four-gear symmetric transmission mechanisms in drilling rigs. The spring stiffness method is utilized to analyze multi-bolt load distribution. A finite element model of the bolted connection is developed by integrating the structural shape and transmission mechanism. The model's accuracy is validated through a rack strain test under various loads. Based on the finite element analysis results, this study proposes an evaluation method for bolted connections using relative deformation difference. The impacts of bolt pitch, end distance, preload, and thickness of the derrick connection plate under different load positions on the connection are examined. This paper presents a methodology and conclusions that can inform the design of bolted connections for heavy-duty drive systems. [ABSTRACT FROM AUTHOR]

In order to study the vibration control effects of floating rail fasteners on the metro depot and its over-track buildings, a metro depot and its over-track buildings are selected for field test and the track-stratum-operation depot-platform coupling finite element calculation model is established and verified by the measured vibration response data. The track-stratum-operation depot-platform-buildings coupling large-scale dynamic finite element analysis model is established based on the extension of the former, and the reaction forces of ordinary fasteners and floating rail fasteners are input into the model respectively to solve the vibration responses of the over-track buildings and analyze the insertion loss of vibration level varying with frequency and maximum Z vibration level in order that the effects of floating rail fasteners on the vibration control of the over-track buildings are evaluated. The research shows that for the building forms studied in this paper, the significant vibration frequency of building floor is 31. 5 Hz when the vibration propagates to the over-track buildings through the depot and the over-track platform. The floating rail fastener has few obvious effects on vibration control at the vibration significant frequency 31. 5 Hz of this over-track buildings, mainly because its stable vibration reduction frequency band occurs above 31.5 Hz. The control effects are optimal near 63 Hz, and the maximum vibration reduction of 15 dB can be achieved. With the floating rail fasteners, the maximum Z vibration level of the over-track building floor generally decreases by 2-5 dB. The research results of this paper can provide reference and basis for the selection and design of damping track for the vibration control of the depot and over-track buildings. [ABSTRACT FROM AUTHOR]

In this paper, a real-time railway fastener detection system using a high-speed laser range finder camera is presented. First, an extensive analysis of various methods based on pixel-wise and histogram similarities are conducted on a specific railway route. Then, a fusing stage is introduced which combines least correlated approaches also considering the performance upgrade after fusing. Then, the resulting method is tested on a larger database collected from a different railway route. After observing repeated successes, the method is implemented on NI LabVIEW and run real-time with a high-speed 3-D camera placed under a railway carriage designed for railway quality inspection. [ABSTRACT FROM PUBLISHER]

Because of their superior mechanical properties, preformed helical fittings are widely used in UHV transmission lines. However, they easily slip and become loose under extreme environments, so it is very important to study the fastening characteristics of preformed helical fittings. According to the stress characteristics of preformed helical fittings, a parametric finite element model including a core and preformed armor rods was established. Finally, the finite element model calculation was verified by comparing it with the test results. In this paper, the influences of the preformed armor rod diameter, pitch, length and forming aperture on the fastening characteristics were investigated. The numerical simulation results showed that the smaller the forming aperture of preformed armor rods, the larger the grip force. However, a small forming aperture is inconvenient to install, and too large of a grip force on the core easily leads to core damage. With the increase in the preformed armor rod length, the grip force increased gradually and linearly, and the increase slowed after the pitch number reached 9. The larger the pitch, the smaller the grip force of the preformed helical fittings. The fastening characteristics of preformed armor rods with slightly larger diameters were better and the fourth power of the diameter of the preformed armor rods has a linear relationship with the grip force. [ABSTRACT FROM AUTHOR]

In tolerance analysis, the effect of clearance fits is especially difficult to estimate because the mating parts are not necessarily in actual contact and can take an infinite number of relative positions. The treatment of these situations is allowed in most of the available methods, possibly introducing additional elements in the dimension chains with appropriate statistical assumptions. The paper provides a similar extension for the static analogy, a previously proposed method that converts the tolerance analysis problem into an equivalent problem of force analysis. The procedure represents each fit, possibly between patterns of features (e.g., fasteners and holes), with a proper constraint in the equivalent static model. The ability of the constraint to transmit forces and torques is determined according to the types and directions of misalignments allowed by the joint clearance. With simple rules, this avoids complications in the static model, which must include only the constraint between parts rather than the geometric details of the mating features. The extended method, currently limited to 2D dimension chains, is demonstrated on examples involving both dimensional and geometric tolerances. The comparison with existing methods shows the correctness of the proposed procedure. The simplicity of the workflow confirms the possibility, already demonstrated for the static analogy, of avoiding numerical simulations or even the use of computer-based tools. [ABSTRACT FROM AUTHOR]

Railway track is a linearly inhomogeneous object that consists of geometrical and elastic discontinuities such as bridges, transition zones, rail joints and crossings. The zones are subjected to the development of local instabilities due to quicker deterioration than the other tracks. Until now, there have been no efficient approaches that could fully exclude the problem of accelerated differential settlements in the problem zones. Many structural countermeasures are directed at controlling the sleeper/ballast loading with the help of fastenings/under-sleeper pad elasticities, sleeper forms and additional bending stiffness reinforcements. However, the efficiency of the methods is difficult to compare. The current paper presents a systematic approach in which the loading distribution effect in the rail support by application of two bending reinforcement methods is compared: auxiliary rail and under-sleeper beam. The study considers only the static effects to reach a clear understanding the influence of the main factors. The track equivalent bending stiffness criterion is proposed for comparing reinforcement solutions. The analysis shows that the activation of the bending stiffness of the reinforcement beams depends on the relative ratio of the rail fastenings stiffness and track support stiffness under sleepers (or under the under-sleeper beam). The comparison demonstrates that conventional auxiliary rail reinforcement solutions are ineffective due to their weak bending because of the high elasticity of fastening clips and the main rail fastenings. The share of an auxiliary rail is maximally 20% in the track bending stiffness and cannot be significantly improved by additional rails. The under-sleeper beam-based reinforcement solutions show noticeably higher efficiency. The highest effect can be achieved by the activation of the horizontal shear interaction between the under-sleeper beam and the rail. The additional track bending stiffness of the under-sleeper-based solutions is about 3.5 times more of the rail one and could be potentially increased to 6–10 times. [ABSTRACT FROM AUTHOR]

Threaded fastening operations are widely used in assembly and are typically time-consuming and costly. In low-volume, high-value manufacturing, fastening operations are carried out manually by skilled workers. The existing approaches are found to be less flexible and robust for performing assembly in a less structured industrial environment. This paper introduces a novel algorithm for detecting the position and orientation of threaded holes and a new method for tightening bolts. First, the elliptic arc fitting method and the three-point method are used to estimate the initial position and orientation of the threaded hole, and the force impact caused by switching from the free space to the constrained space during bolt tightening is solved. Second, by monitoring the deformation of passive compliance, the position information is introduced into the control process to better control the radial force between the bolt and the threaded hole in the tightening process. The constant force controller and orientation compliance controller are designed according to the adaptive control theory. A series of experiments are carried out. The results show that the proposed method can estimate the initial position and orientation of an M24 bolt with an average position error of 0.36 mm, 0.43 mm and 0.46 mm and an orientation error of 0.65°, 0.46° and 0.59°, and it can tighten the bolt with a success rate of 98.5%. [ABSTRACT FROM AUTHOR]

Quality assessment of stiffness and load-carrying capacity of composite connections is of great importance when it comes to designing timber–concrete composite structures. The new European regulation intended explicitly for timber–concrete structures has made a significant contribution to this field, considering that until today there was no adequate design standard. Due to the proposed general expressions for determining the stiffness and load-carrying capacity of composite connections made with dowel-type fasteners, which are incapable of describing most of the commonly applied fasteners, engineering, and scientific practice remained deprived of a quality assessment of the essential mechanical properties of the connection. In order to overcome this problem, this paper proposes a numerical model of the connection suitable for determining the whole load–slip curve, allowing it to estimate the stiffness and load-carrying capacity of the connection. The model was developed by considering the non-linear behavior of timber and fasteners, which is determined through simple experimental tests. For the numerical model validation, experimental tests were carried out at the level of the applied materials and on the models of the composite connection. Through numerical simulations, analysis of obtained results, and comparison with experimental values, it can be confirmed that it is possible to simulate the pronounced non-linear behavior of the timber–concrete connection using the proposed model. The estimated values of stiffness and load-carrying capacity are in agreement with the conducted experimental testing. At the same time, the deviations are much less than the ones obtained from recommendations given by the new regulation. Additionally, apart from evaluating the value and the simulation of the complete curve, it is possible to determine local effects, such as the crushing depth in timber and concrete, the fastener's rotation, and the participation of forces in the final capacity of the connection. [ABSTRACT FROM AUTHOR]

To this day, most of the papers related to hybrid joints were focused on single and double lap joints in which shear deformation and degradation was the dominant phenomenon. However, in real constructions, complex state of loads can be created by: a) torsion with shear, b) bending with shear, c) torsion with tensile. Analytical and numerical computation for simple mechanical joints is known, however, the introduction of an adhesive layer to this joint makes the load transferred both through: (1) the adhesive and (2) mechanical fasteners. There is also an interaction between the amount and stiffness of mechanical fasteners and the strength of the adhesive layer. The paper presents the results of numerical calculations for the bending with shear type of load for the hybrid structural joint and corresponding simple joints by: (1) pure adhesion and (2) rivets with different quantity maintaining the same cross-sectional area. A total of 9 simulations were performed for: (1) 4 types of pure rivets connections, (2) pure adhesive joint and (3) 4 kinds of hybrid joints. The surface-based cohesive behavior was used for creation of the adhesive layer, whereas the rivets were modelled by connector type fasteners, which simplify complexity of the numerical model. The use of connectors allowed for effort assessment taking into account damage in both types of connections. Application of connector elements can be useful for larger structures modelling, e.g. aircraft fuselage, where the number of mechanical joints is significant and complex load conditions occur. [ABSTRACT FROM AUTHOR]