A Railway Fastener Inspection Method Based on Abnormal Sample Generation

Regular fastener detection is necessary to ensure the safety of railways.However,the number of abnormal fasteners is significantly lower than the number of normal fasteners in real railways.Existing supervised inspectionmethods have insufficient detection ability in cases of imbalanced samples.To solve this problem,we propose an approach based on deep convolutional neural networks(DCNNs),which consists of three stages:fastener localization,abnormal fastener sample generation based on saliency detection,and fastener state inspection.First,a lightweight YOLOv5s is designed to achieve fast and precise localization of fastener regions.Then,the foreground clip region of a fastener image is extracted by the designed fastener saliency detection network(F-SDNet),combined with data augmentation to generate a large number of abnormal fastener samples and balance the number of abnormal and normal samples.Finally,a fastener inspection model called Fastener ResNet-8 is constructed by being trained with the augmented fastener dataset.Results show the effectiveness of our proposed method in solving the problem of sample imbalance in fastener detection.Qualitative and quantitative comparisons show that the proposed F-SDNet outperforms other state-of-the-art methods in clip region extraction,reaching MAE and max F-measure of 0.0215 and 0.9635,respectively.In addition,the FPS of the fastener state inspection model reached 86.2,and the average accuracy reached 98.7%on 614 augmented fastener test sets and 99.9%on 7505 real fastener datasets.

Regression Method for Rail Fastener Tightness Based on Center-Line Projection Distance Feature and Neural Network

In the railway system,fasteners have the functions of damping,maintaining the track distance,and adjusting the track level.Therefore,routine maintenance and inspection of fasteners are important to ensure the safe operation of track lines.Currently,assessment methods for fastener tightness include manual observation,acoustic wave detection,and image detection.There are limitations such as low accuracy and efficiency,easy interference and misjudgment,and a lack of accurate,stable,and fast detection methods.Aiming at the small deformation characteristics and large elastic change of fasteners from full loosening to full tightening,this study proposes high-precision surface-structured light technology for fastener detection and fastener deformation feature extraction based on the center-line projection distance and a fastener tightness regression method based on neural networks.First,the method uses a 3D camera to obtain a fastener point cloud and then segments the elastic rod area based on the iterative closest point algorithm registration.Principal component analysis is used to calculate the normal vector of the segmented elastic rod surface and extract the point on the centerline of the elastic rod.The point is projected onto the upper surface of the bolt to calculate the projection distance.Subsequently,the mapping relationship between the projection distance sequence and fastener tightness is established,and the influence of each parameter on the fastener tightness prediction is analyzed.Finally,by setting up a fastener detection scene in the track experimental base,collecting data,and completing the algorithm verification,the results showed that the deviation between the fastener tightness regression value obtained after the algorithm processing and the actual measured value RMSE was 0.2196 mm,which significantly improved the effect compared with other tightness detection methods,and realized an effective fastener tightness regression.

Research on Track Fastener Service Status Detection Based on Improved Yolov4 Model

As an important part of railway lines, the healthy service status of track fasteners was very important to ensure the safety of trains. The application of deep learning algorithms was becoming an important method to realize its state detection. However, there was often a deficiency that the detection accuracy and calculation speed of model were difficult to balance, when the traditional deep learning model is used to detect the service state of track fasteners. Targeting this issue, an improved Yolov4 model for detecting the service status of track fasteners was proposed. Firstly, the Mixup data augmentation technology was introduced into Yolov4 model to enhance the generalization ability of model. Secondly, the MobileNet-V2 lightweight network was employed in lieu of the CSPDarknet53 network as the backbone, thereby reducing the number of algorithm parameters and improving the model’s computational efficiency. Finally, the SE attention mechanism was incorporated to boost the importance of rail fastener identification by emphasizing relevant image features, ensuring that the network’s focus was primarily on the fasteners being inspected. The algorithm achieved both high precision and high speed operation of the rail fastener service state detection, while realizing the lightweight of model. The experimental results revealed that, the MAP value of the rail fastener service state detection algorithm based on the improved Yolov4 model reaches 83.2%, which is 2.83% higher than that of the traditional Yolov4 model, and the calculation speed was improved by 67.39%. Compared with the traditional Yolov4 model, the proposed method achieved the collaborative optimization of detection accuracy and calculation speed.

Corrosion Test of the Steel Plate in a WJ-8 Fastener for High Speed Rail

It was found that the steel plate in the composite plate in the WJ-8 fastener used in high speed rail is rusty. The objective of this study is to test the zinc coating of the steel plate. A literature review was conducted to identify the zinc coating techniques, and the companies that can provide different coating service was identified. A salt fog chamber was built that was in compliance with the ANSI B117 code, and the steel plates that were coated by the identified companies were tested using the salt fog chamber. The results indicated that the coating technique that had the best performance in preventing corrosion was the Greenkote plates with passivation. The galvanized option had the roughest coating layer, and it was the most reactive in the salt water solution. This makes it non-ideal for the dynamic rail environment because the increased friction of the plate could damage the supports, especially during extreme temperatures that would cause the rail to expand or contract. Greenkote with Phosphate and ArmorGalv also provided increased corrosion prevention with a smooth, strong finish, but it had more rust on the surface area than the Greenkote with ELU passivation. The ArmorGalv sample had more rust on the surface area than the Greenkote samples. This may not be a weakness in the ArmorGalv process;rather, it likely was the result of this particular sample not having the added protection of a colored coating.

Rail fastener detection of heavy railway based on deep learning

Image detection based on machine learning and deep learning currently has a good application prospect for railway fault diagnosis,with good performance in feature extraction and the accuracy of image localization and good classification results.To improve the speed of locating small target objects of fasteners,the YOLOv5 framework model with faster algorithm speed is selected.To improve the classification accuracy of fasteners,YOLOv5-based heavy-duty railway rail fastener detection is proposed.The anchor size is modified on the original basis to improve the attention to small targets of fasteners.The CBAM(Convolutional Block Attention Module)module and TPH(Transformer Prediction Head)module are introduced to improve the speed and accuracy issues.The rail fasteners are divided into 6 categories.Experiment comparisons show that before the improvement,the MAP@0.5 value of all categories are close to the peak of 0.989 after the epoch of 150,and the F1 score approaches 1 with confidence in the interval(0.2,0.95).The improved mAP@0.5 value approached the highest value of 0.991 after the epoch of 75,and the F1 score approached 1 with confidence in the interval(0.01,0.95).The experiment results indicate that the improved YOLOv5 model proposed in this paper is more suitable for the task of detecting rail fasteners.

YOLO-O2E:A Variant YOLO Model for Anomalous Rail Fastening Detection

Rail fasteners are a crucial component of the railway transportation safety system.These fasteners,distinguished by their high length-to-width ratio,frequently encounter elevated failure rates,necessitating manual inspection and maintenance.Manual inspection not only consumes time but also poses the risk of potential oversights.With the advancement of deep learning technology in rail fasteners,challenges such as the complex background of rail fasteners and the similarity in their states are addressed.We have proposed an efficient and high-precision rail fastener detection algorithm,named YOLO-O2E(you only look once-O2E).Firstly,we propose the EFOV(Enhanced Field of View)structure,aiming to adjust the effective receptive field size of convolutional kernels to enhance insensitivity to small spatial variations.Additionally,The OD_MP(ODConv and MP_2)and EMA(EfficientMulti-Scale Attention)modules mentioned in the algorithm can acquire a wider spectrum of contextual information,enhancing the model’s ability to recognize and locate objectives.Additionally,we collected and prepared the GKA dataset,sourced from real train tracks.Through testing on the GKA dataset and the publicly available NUE-DET dataset,our method outperforms general-purpose object detection algorithms.On the GKA dataset,our model achieved a mAP 0.5 value of 97.6%and a mAP 0.5:0.95 value of 83.9%,demonstrating excellent inference speed.YOLO-O2E is an algorithm for detecting anomalies in railway fasteners that is applicable in practical industrial settings,addressing the industry gap in rail fastener detection.

Parametric investigation of railway fastenings into the formation and mitigation of short pitch corrugation

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.

Effect of laser shock processing on fatigue life of fastener hole

The fatigue properties of laser shock processing （LSP） on both side surfaces of fastener hole with diameter of 3 mm in the LY12CZ aluminum alloy specimens were investigated. The superficial residual stress was measured by X-ray diffraction method. Fatigue experiments of specimens with and without LSP were performed, and the microstructural features of fracture of specimens were characterized by scanning electron microscopy （SEM）. The results indicate that the compressive residual stress can be induced into the surface of specimen, and the fatigue life of the specimen with LSP is 3.5 times as long as that of specimen without LSP. The location of fatigue crack initiation is transferred from the top surface to the sub-surface after LSP, and the fatigue striation spacing of the treated specimen during the expanding fatigue crack is narrower than that of the untreated specimen. Furthermore, the diameters of the dimples on the fatigue crack rupture zone of the specimen with LSP are relatively bigger, which is related to the serious plastic deformation in the material with LSP.

Applicability of small resistance fastener on long-span continuous bridges of high-speed railway

Ballastless tracks have been widely applied in high-speed railway (HSR). The adaptability research between continuous welded rails (CWR) and long-span bridges of HSR is of great practical engineering significance. Based on the HSR long-span continuous bridges, the integrative spatial finite element model of track-bridge-pier-foundation system was established with the nonlinear spring element simulating the longitudinal resistance between track and bridge. Comparative study on the various additional longitudinal forces of CWR using the common fasteners and small resistance fasteners was carried out. Analysis results indicate that the additional expansion forces and additional rail-breaking forces in long-span ballastless continuous girders can be reduced evidently by 40% 50% after adopting small resistance fasteners, but lead to greater rail broken gap. The small resistance fasteners have little influence on the additional force only caused by vertical load, but can reduce the additional force caused by vertical load combined with braking load by over 10%. Besides, transient analysis method is proved to be more accurate and safe in calculating additional longitudinal forces when the train running or braking on the bridge, compared with the traditional static method.

Derailment risk and dynamics of railway vehicles in curved tracks： Analysis of the effect of failed fasteners

The effect of the fastener＇s failure in a railway track on the dynamic forces produced in the wheel-rail contact is studied using the simulation software VAMPIRE to assess the derailment risk of two different vehicles in two curves with distinct characteristics. First, a 3D-FEM model of a real track is constructed, paying special attention to fasteners, and calibrated with displacement data obtained experimentally during a train passage. This numerical model is subsequently used to determine the track vertical and lateral stiffness. This study evidences that although the track can practically lose its lateral stiffness as a consequence of the failure of 7 consecutive fasteners, the vehicle stability would not be necessarily compromised in the flawed zone. Moreover, the results reveal that the uncompensated acceleration and the distance along which the fasteners are failed play an important role in the dynamic behavior of the vehicle-track system, influencing strongly the risk of derailment.

The Use of Cavitation Peening to Increase the Fatigue Strength of Duralumin Plates Containing Fastener Holes

An effective method for improving the fatigue life of Duralumin plates with fastener holes, such as those used in the construction of aircraft, is to introduce a compressive residual stress around the fastener holes. Cavitation peening is a novel peening method that uses the cavitation impact produced when a high-speed water jet is injected into a water-filled chamber. In this paper, Duralumin plate specimens with holes were treated by cavitation peening under various conditions, and the fatigue strength of the specimens was determined using a plate bending fatigue test. It was revealed that a compressive residual stress was introduced not only on surfaces perpendicular to the axis of the cavitating jet but also on the walls of holes which were parallel to this. It was found that a 51% improvement in fatigue strength could be achieved by cavitation peening. Note that this is first report demonstrating an improvement in the fatigue life of Duralumin plates with fastener holes by cavitation peening.

A study on joining magnesium alloy high pressure die casting components with thread forming fasteners

With tremendous weight saving potential,magnesium alloy high pressure die casting components have been widely used for automotive applications.Magnesium fastening technology is thus becoming increasingly important to design engineers.Joining as-cast holes of magnesium high pressure die casting components with thread forming fasteners provides significant advantages for the assembly process,overall cost benefit and joint integrity.This type of joint is thus preferred for structural applications.Designing the thread forming fasteners with as-cast holes follows the general rules for designing for the machined holes,including carefully designing the variables such as the assembly torque range,the length of thread engagement,the hole diameter and required failure mode.In addition,special attention needs to be paid to the draft angles of the magnesium cast components that are required for the die casting process.In this paper,the effects of above key factors,individually and combined,on the joint performance of thread forming fasteners with as-cast blind holes of AM60B magnesium components are studied.A joint design philosophy was proposed to optimize both joint performance(the prevailing torque,the failure torque and the failure mode)and manufacturing easiness(the hole diameter and corresponding draft angle).The detailed design considerations for as-cast holes of magnesium HPDC are discussed and explained through a hypothetical example.

Online Rail Fastener Detection Based on YOLO Network

Traveling by high-speed rail and railway transportation have become an important part of people’s life and social production.Track is the basic equipment of railway transportation,and its performance directly affects the service lifetime of railway lines and vehicles.The anomaly detection of rail fasteners is in a priority,while the traditional manual method is extremely inefficient and dangerous to workers.Therefore,this paper introduces efficient computer vision into the railway detection system not only to locate the normal fasteners,but also to recognize the fasteners states.To be more specific,this paper mainly studies the rail fastener detection based on improved You can Only Look Once version 5(YOLOv5)network,and completes the real-time classification of fastener states.The improved YOLOv5 network proposed contains five sections,which are Input,Backbone,Neck,Head Detector and a read-only Few-shot Example Learning module.The main purpose of this project is to improve the detection precision and shorten the detection time.Ultimately,the rail fastener detection system proposed in this paper is confirmed to be superior to other advanced algorithms.This model achieves on-line fastener detection by completing the“sampling-detection-recognition-warning”cycle of a single sample before the next image is sampled.Specifically,the mean average precision of model reaches 94.6%.And the model proposed reaches the speed of 12 ms per image in the deployment environment of NVIDIA GTX1080Ti GPU.

Experimental and Numerical Investigation on the Mechanical Behaviors of the Velcro^(■) and Dual-Lock Fasteners

In order to characterize the mechanical behaviors of the Velcro~? and Dual-lock fasteners, a series of tests including the butt-joint(BJ) monotonic tensile and shear, mixed tensile-shear with various loading angles, the loading rates effects, the double cantilever beam(DCB) fracture and 180° peel experiments were performed. The tensile and shear tests results showed that the mechanical behaviors of Velcro~? fastener separation are analogous to ductile materials, and those of Dual-lock fasteners are more like brittle ones. The mixed tensile-shear with various loading angles tests results demonstrated that magnitudes of the peak stresses in 30°, 45°, and 60° have no significant differences, which are lower than those in the monotonic tensile or shear tests for the two fasteners. The effects of the loading rate tests show that the peak stresses of the Velcro~? fastener manifested good performance at the loading rate of 10 to 20 mm/min in the tensile and shear conditions, and the Dual-lock did it well around the loading rates of 10 to 20 mm/min in the tensile condition. The cohesive zone model(CZM) is employed to numerical predict the DCB fracture and the 180° peel tests. The CZM predictions results are proven to commendably capture the two tests separation processes, of the tow fasteners, and the numerical results agreed well with the peeling tests data of the Dual lock fasteners. The results and discussions in this study are expected to bring more understanding to engineers and designers about the performance of Velcro~? and Dual lock fasteners.

Rail fastener defect inspection method for multi railways based on machine vision

Purpose–This research aims to improve the performance of rail fastener defect inspection method for multi railways,to effectively ensure the safety of railway operation.Design/methodology/approach–Firstly,a fastener region location method based on online learning strategy was proposed,which can locate fastener regions according to the prior knowledge of track image and template matching method.Online learning strategy is used to update the template library dynamically,so that the method not only can locate fastener regions in the track images of multi railways,but also can automatically collect and annotate fastener samples.Secondly,a fastener defect recognition method based on deep convolutional neural network was proposed.The structure of recognition network was designed according to the smaller size and the relatively single content of the fastener region.The data augmentation method based on the sample random sorting strategy is adopted to reduce the impact of the imbalance of sample size on recognition performance.Findings–Test verification of the proposed method is conducted based on the rail fastener datasets of multi railways.Specifically,fastener location module has achieved an average detection rate of 99.36%,and fastener defect recognition module has achieved an average precision of 96.82%.Originality/value–The proposed method can accurately locate fastener regions and identify fastener defect in the track images of different railways,which has high reliability and strong adaptability to multi railways.

The Effects of Fatigue Cracks on Fastener Loads during Cyclic Loading and on the Stresses Used for Crack Growth Analysis in Classical Linear Elastic Fracture Mechanics Approaches

High strength threaded fasteners are widely used in the aircraft industry, and service experience shows that for structures where shear loading of the joints is significant, like skin splices, fuselage joints or spar caps-web attachments, more cracks are initiated and grow from the edges of the fastener holes than from features like fillets radii and corners or from large access holes. The main causes of this cracking are the stress concentrations introduced by the fastener holes and by the threaded fasteners themselves, with the most common damage site being at the edge of the fastener holes. Intuitively, it is easy to visualize that after the crack initiation, during the growth stages, some of the load transferred initially by the fastener at the cracked hole will decrease, and it will be shed to the adjacent fasteners that will carry higher loads than in uncracked condition. Using currently available computer software, the method presented in this paper provides a relatively quick and quantitatively defined solution to account for the effects of crack length on the fastener loads transfer, and on the far field and bypass loads at each fastener adjacent to the crack. At each location, these variations are determined from the 3-dimensional distribution of stresses in the joint, and accounting for secondary bending effects and fastener tilt. Two cases of a typical skins lap splice with eight fasteners in a two rows configuration loaded in tension are presented and discussed, one representative for wing or fuselage skins configurations, and the second case representative for cost effective laboratory testing. Each case presents five cracking scenarios, with the cracks growing from approx. 0.03 inch to either the free edge, next hole or both simultaneously.

Thermal entanglement in a spin-1/2 Ising-Heisenberg butterfly-shaped chain with impurities

We investigate the effect of impurities on the thermal entanglement in a spin-1/2 Ising-Heisenberg butterfly-shaped chain,where four interstitial Heisenberg spins are localized on the vertices of a rectangular plaquette in a unit block.By using the transfer-matrix approach,we numerically calculate the partition function and the reduced density matrix of this model.The bipartite thermal entanglement between different Heisenberg spin pairs is quantified by the concurrence.We also discuss the fluctuations caused by the impurities through the uniform distribution and the Gaussian distribution.Considering the effects of the external magnetic field,temperature,Heisenberg and Ising interactions as well as the parameter of anisotropy on the thermal entanglement,our results show that comparing with the case of the clean model,in both the twoimpurity model and the impurity fluctuation model the entanglement is more robust within a certain range of anisotropic parameters and the region of the magnetic field where the entanglement occurred is also larger.

Self-Sensing TDR for Bearing Failure Detection of CFRP Laminate Fastener Hole with Particular Reference to the Effect of Fasteners

Carbon fiber reinforced polymer composites (CFRP) have been applied to aerospace and automobile structures. For many CFRP structures, mechanical metallic fasteners are usually adopted. For the fasteners used in internal structures such as a wing box, the damage to the CFRP structures around fastener holes is visually quite difficult to find. A simple method to find the damage around fastener holes is required. In this study a self-sensing time domain reflectometry (TDR) method is newly applied to detect bearing failure around the fastener holes of CFRP structures. A microstrip-line method is generally used to create a transmission line. When the transmission line is mounted near the metallic fasteners, they may affect the impedance of the transmission line. In this study, the effect of distance between the fasteners and the transmission line was numerically investigated using a finite difference time domain analysis method. After finding the appropriate distance, experiments were performed to detect the bearing failure around a fastener hole. The experiments showed the performance of the self-sensing TDR for detecting bearing failure.

Multi-species particle swarms optimization based on orthogonal learning and its application for optimal design of a butterfly-shaped patch antenna

A new multi-species particle swarm optimization with a two-level hierarchical topology and the orthogonal learning strategy(OMSPSO) is proposed, which enhances the global search ability of particles and increases their convergence rates. The numerical results on 10 benchmark functions demonstrated the effectiveness of our proposed algorithm. Then, the proposed algorithm is presented to design a butterfly-shaped microstrip patch antenna. Combined with the HFSS solver, a butterfly-shaped patch antenna with a bandwidth of about 40.1% is designed by using the proposed OMSPSO. The return loss of the butterfly-shaped antenna is greater than 10 d B between 4.15 and 6.36 GHz. The antenna can serve simultaneously for the high-speed wireless computer networks(5.15–5.35 GHz) and the RFID systems(5.8 GHz).

Fastener region location method based on gray-scale mutation

Rail fastener positioning method has high requirements for image quality and positioning accuracy.Therefore,a rail fastener positioning method based on gray mutation is proposed.Firstly,the image is denoised by an improved median filter.Then,according to the characteristics of image frequency domain,the image is decomposed by wavelet transfrom to extract low-frequency component,and the low-frequency component is filtered and processed by gamma transformation to reduce the influence of natural environment factors on image quality.After that,according to the change rule of gray-scale values of different regions in the image,the gray-scale mutation in column and line directions of the image is statistically analyzed,and then the rail surface and the sleeper are located.Finally,the fastener area is accurately located by using the position relationship of the rail surface,the sleeper and the fastener in the image.The experimental results show that the positioning accuracy of the proposed method is 93.19%,which can quickly and effectively locate the fastener region,and has strong environmental adaptability,robustness and practicability.