Automatic Rail Research Articles

Novel Coil Transducer Induced Thermoacoustic Detection of Rail Internal Defects Towards Intelligent Processing

A novel resonant tri-coil transducer is proposed to induce thermoacoustic (TA) signals for detecting rail internal defects. It consists of a ferrite plate-backed tri-coil and its associated circuits, a shielding layer, and a three-dimensional printed enclosure for supporting and securing. General TA detection theory is illustrated to understand how to detect rail internal defects. For achieving electromagnetic (EM) field shaping and focusing, the physical structure of the proposed transducer is optimized. It reduces backward EM fields and interference. Then, an equivalent circuit model between the proposed transducer and the rail is constructed to analyze optimal power transfer efficiency as a function of distance and frequency. To validate the design concept, the proposed transducer is fabricated for inducing TA signals, and experiments are performed. Several engineered rail specimens (UIC-60E) with different types of defects are prepared. The induced TA waves are found to be compressional. With selected features, a support vector machine algorithm-based multinomial classifier model is trained and employed for intelligent processing. Then, the proposed transducer is integrated into the self-designed automatic rail monitoring system. Experimental results show that the self-designed system can achieve an accuracy rate of 96.4%, successfully identifying defects in defect classification. The proposed transducer has low cost in fabrication and maintenance, focused magnetic fields with a large lift-off distance and enhanced acoustic wave intensity. Compared to earlier version of the system, the spacing between the transmitter and the rail has been further increased.

A real-time automatic rail extraction algorithm for low-density mobile laser scanning data

Automatic detection of railroad infrastructure using Mobile Laser Scanning systems is a key technology for both advanced rail driver assistance and intelligent track maintenance. The recent research into railway facility extraction or condition monitoring usually relies on high-density point cloud dataset with known sensor parameters but ignores the processing performance in actual deployment and has high requirements for the acquisition device. To address these limitations, a novel rail extraction algorithm is proposed for processing Mobile Laser Scanning data in real time during acquisition, which could extract rail features by a hierarchical coarse-to-fine method with basic structural parameters. Using the geometric and global statistical characteristics of rail in raw data, a new rail descriptor is defined based on the quantitative statistics of points satisfying height difference in generalized local neighborhood. The approach is evaluated experimentally by a simulated real-time acquisition data and compared with a reference method. The experimental results show that the proposed algorithm has a finer extraction effect and good real-time performance.

Automatic Signal and Gate System

Abstract: The "Automatic Signal and Gate System" project is a hardware setup aimed at improving safety on roads, particularly in hilly areas and railway crossings. By utilizing an Arduino UNO and an IR sensor module, the system focuses on signaling in challenging terrains, emphasizing the Automatic Rail Gate Signaling System to prevent rail crossing accidents. This project addresses the pressing issue of accidents and interruptions in rail tracks, which are prevalent in India, a densely populated country. Through the implementation of IR sensors, the system detects the presence of trains and transmits signals to the Arduino. The Arduino, in turn, controls the gate and signals using a servo motor, effectively enhancing safety measures at rail crossings. With a strong emphasis on this specific concept, the project aims to minimize rail crossing accidents and optimize the efficiency of rail gates and crossings. By concentrating efforts on this critical aspect, the system strives to mitigate accidents, reducing unnecessary interruptions in rail track operations and maximizing safety for commuters and railway personnel alike

Automatic Rail Detection Technology Based on PointNet++ Using 3D Point Cloud Data of Railway Bridges

Recently, railway maintenance has been receiving significant attention to prevent railway accidents. Accordingly, various methods are being developed that apply IT to railroad maintenance, and digital models can be used for an efficient management. To develop a railroad digital model, current status information of the rail is required. However, the existing method consumes considerable time and cost. Therefore, in this study, we proposed a system to scan the railroad using a UAV and automatically detect the rail using PointNet++. The proposed system consisted of Phase 1 (structure from motion) and Phase 2 (rail detection). To verify the performance of the proposed system, the railroad bridge of the Osong test track in Nojang-ri, Jeondong-myeon, Sejong City, South Korea, was targeted. The proposed system is expected to be utilized in various fields such as damage detection, simulation, predictive maintenance, and efficient operation management.

Automatic Rail Surface Defect Inspection Using the Pixelwise Semantic Segmentation Model

Rail surface defect (RSD) is an important railroad track quality indicator and impacts the overall track safety and ride quality. Railroads need to inspect and evaluate RSD conditions regularly to make proper maintenance plan for both freight and passenger services. Unfortunately, current field practices for rail surface defect defection have not been fully automated yet and significant amount of manual inspections are still involved, which could be labor-intensive but low-efficient. Earlier efforts have proposed multiple automatic rail surface defect detection systems, but the accuracy and equipment cost issues have limited their applications in the field. To address the needs in track inspections, this study proposes an improved Deeplabv3-plus model using a lightweight backbone, attention module, and the Lovász-Softmax loss to automatically detect RSD. The improved model not only improves the detection performance but also keeps the computational cost manageable. The ResNet-18 backbone is adopted for the encoder design to reduce the computational cost and maximize the inference speed. The CBAM attention module is unified with the decoder structure for critical features’ representation. The optimum crop size is determined in the training process. The Lovász-Softmax loss is implemented to address the severe class imbalance issue. The improved DeepLabv3-plus model is compared with five other models to validate the performance. Experimental results on both original data and noisy data confirm the proposed model achieves the best performance on evaluation metrics and visualization. This work provides a feasible solution for the future implementation of automatic railroad track inspection.

System Design, Analysis, and Control of an Intelligent Vehicle for Transportation in Greenhouse

Smart agriculture represents a significant trend in agricultural development, given its potential to enhance operational efficiency and reduce labor intensity. Despite the adoption of modern greenhouse technologies, such as sensors and automation systems, crop transportation is still largely achieved through manual labor, largely due to the complex environment and narrow terrain of greenhouses. To address this challenge, this work proposes the design of an intelligent vehicle that is capable of transporting crops in a commercial greenhouse, with the aim of improving operational efficiency and reducing labor intensity. To enable the vehicle to navigate the horizontal and rail surfaces within the greenhouse, a novel chassis structure is designed that is capable of simultaneous driving on both ground and rail surfaces. Additionally, the two-dimensional codes is adopted for positioning and navigation, thereby avoiding the need to modify existing greenhouse road surfaces. Through the implementation of a comprehensive system-control strategy, the intelligent vehicle realized various functions, including ground driving, rail driving, moving up and down the rail, and automatic rail changing. Experimental results demonstrate that the designed intelligent vehicle successfully meets the basic requirements for crop transportation in a greenhouse, providing a solid foundation for future unmanned operations.

The Automatic Rail Surface Multi-Flaw Identification Based on a Deep Learning Powered Framework

Rails of unhealthy conditions are considered as major targets in the rail surface inspection and this study focuses on inspecting five types of major rail surface flaws, corrugations, defects, the shelling, squats, and grinding marks, via analyzing railway images. We propose a deep learning powered rail surface multi-flaw identification framework composed of two main components, a novel rail extractor for extracting rails from the background and a cascading rail surface flaw identifier for precisely identifying different flaws. The novelty of the cascading rail surface flaw identifier includes: 1) An unhealthy rail detector developed based on a DenseNet backbone for recognizing the healthy/unhealthy status on rail surfaces and 2) A rail flaw classifier for identifying flaw types on unhealthy rail surfaces. A new feature joint learning process integrating latent features derived from selected hierarchies of the DenseNet backbone as well as two traditional feature extractors, the local binary pattern and the gray level co-occurrence matrix, is developed to facilitate the rail flaw classifier to offer accurate identification results. The effectiveness of the proposed framework for rail surface multi-flaw identification is validated with datasets provided by an industrial partner in China and collected from online sources. Based on collected datasets, the proposed framework is capable to identify the rail with unhealthy conditions and its flaw type. The overall identification performance can achieve a 98.2% accuracy. Three groups of benchmarking methods are employed to verify advantages of the proposed framework. Computational results demonstrate the impressive performance of the proposed framework in the rail surface multi-flaw identification and its applicability on new datasets.

An Automated Rail Extraction Framework for Low-Density LiDAR Data Without Sensor Configuration Information

Around one-third of the U.S. rail network is owned and operated by shortlines (Class II and III railroads). The infrastructure conditions of these railroads are marginal, and their revenue and number of employees are insufficient. To conduct proper and timely infrastructure management, it is critical for shortlines to establish a reliable and cost-effective inventory of their existing rail tracks. Significant efforts have been made to develop and employ automatic rail extraction methods as it is a critical step to establishing a rail infrastructure inventory. However, existing methods rely heavily on high-density point cloud datasets with known sensor properties and configurations or sophisticated imaging sensors. These requirements prevent their deployments in shortlines because point cloud data available to shortlines are often in low-density and with unknown specifications constrained by their financial situations. To address those limitations, we propose an automatic, configuration-independent coarse-to-fine extraction method for low-density LiDAR data. The proposed extraction framework is only developed based on high-level geometric features of the rail that can even be captured by low-density point cloud data with unknown sensor properties and configurations. The proposed method is evaluated on the existing grade-crossing dataset collected by the Federal Railroad Administration for grade-crossing safety evaluation with a point cloud density of only 293 points/<inline-formula> <tex-math notation="LaTeX">$\text{m}^{{2}}$ </tex-math></inline-formula>. The overall performance shows average completeness of 96.97&#x0025;, correctness of 99.71&#x0025;, and quality of 96.67&#x0025; for all extraction scenarios. The promising results provide shortlines a capability to extract rails for any geometry measurements based on any low-density LiDAR data available to them.

Компьютерное моделирование грузозахватного устройства автоматизированной системы выгрузки-подачи рельсовых плетей в РСП-1

Purpose: Modernization of rail unloading-supply technology of rail unloading-supply automatic system with the development of multifunctional rail grip and the definition of compliance degree of being used I-beam 36M as a monorail to new requirements for a strength. Methods: Computer modeling of solid virtual model of being designed rail grip is developed in the environment of computer-aided design system SolidWorks. Results: Load-carrying beam of unloading-supply automatic system (USAS) lifting device with a new grip is calculated. It’s found that the I-beam as of 36M grade fully meets required strength and rigidity conditions at installing a new grip on a solid plot. Practical significance: Design of automatic rail grip is designed that’s reckoned upon work with one or three rails simultaneously.

Automatic Rail Component Detection Based on AttnConv-Net

The automatic detection of major rail components using railway images is beneficial to ensure the rail transport safety. In this paper, we propose an attention-powered deep convolutional network (AttnConv-net) to detect multiple rail components including the rail, clips, and bolts. The proposed method consists of a deep convolutional neural network (DCNN) as the backbone, cascading attention blocks (CAB), and two feed forward networks (FFN). Two types of positional embedding are applied to enrich information in latent features extracted from the backbone. Based on processed latent features, the CAB aims to learn the local context of rail components including their categories and component boundaries. Final categories and bounding boxes are generated via two FFN implemented in parallel. To enhance the detection of small components, various data augmentation methods are employed in the training process. The effectiveness of the proposed AttnConv-net is validated with one real dataset and another synthesized dataset. Compared with classic convolutional neural network based methods, our proposed method simplifies the detection pipeline by eliminating the need of prior- and post-processing, which offers a new speed-quality solution to enable faster and more accurate image-based rail component detections

Automatic Rail Surface Defects Inspection Based on Mask R-CNN

Rail surface defects have negative impacts on riding comfort and track safety, and could even lead to accidents. Based on the safety database (2020) of the Federal Railroad Administration (FRA), rail surface defects have been among the main factors causing derailments. During the past decades, there have been many efforts to detect such rail surface defects. However, the applications of earlier methods are limited by the high requirements of specialized equipment and personnel training. To date, rail surface defect inspection is still a very labor-intensive and time-consuming process, which hardly satisfies the field maintenance expectations. Therefore, a cost-effective and user-friendly automatic system that can inspect the rail surface defects with high accuracy is urgently needed. To address this issue, this study proposes a computer vision-based instance segmentation framework for rail surface defect inspection. A rail surface database including 1,040 images (260 source images and 780 augmented images) has been built. The classic instance segmentation model, Mask R-CNN, has been re-trained and fine-tuned for inspecting rail surface defects with the customized dataset. The influences of different backbones and learning rates are investigated and discussed. Experimental results indicate the ResNet101 backbone reaches better inspection capability. With a learning rate of 0.005, the re-trained Mask R-CNN model can achieve the best performance on the bounding box and mask predictions. Sixteen images are used to test the inspection performance of the fine-tuned model. The results are promising and indicate potential field applications in the future.

A Detection System for Rail Defects Based on Machine Vision

In this work, a machine vision based detection system is designed and applied to identify the region of rail track need to be polished. The CCD industrial camera is employed to obtain the RGB color space images on the railway line in real time. To reduce the effect of environmental brightness, the obtained RGB space images are transformed into HSI color space. Then, the noise was removed from the grayscale image by the Gaussian blur to remove the interference elimination of the acquisition signal. According to the calculated saturation and hue values, the threshold of saturation and hue values is set for the following image segmentation, respectively. At last, the accurate position information of the rail track region needs to be polished is obtained for the subsequent grinding processing. By employing the machine vision system, high-efficient automatic rail defects grinding could be realized.

Stacking Position Allocation Model Based on Automatic Rail Crane

In order to reduce the energy consumption of the automated container terminal, based on the operation of the container in the yard, this paper used the travel distance of the automatic rail crane to represent the energy consumption problem to study the problem of container allocation. Under the premise that the operating time of inbound container was known, this paper took the number of pressurized containers as a limiting condition, and established the allocation model of the storage space to minimize the travel distance of the automatic rail crane. Considering that the model had a large calculation scale and was an NP-hard problem, a genetic algorithm was used to solve it. Example analysis results show that the model and algorithm can effectively reduce the travel distance of the automatic rail crane by 17.02% under the premise of controlling the number of pressure boxes.

Automatic Rail Track Surface Anomaly Detection with Smartphone Based Monitoring System

Railroad companies spend a significant portion of their revenue on track inspections to maintain safety and maximize operational efficiency. Deviations from the designed track geometry over time could lead to poor ride quality and possible derailments. The existing approaches to track inspections are expensive and laborious. The use of low-cost sensors aboard revenue service trains to screen the infrastructure for track irregularities could improve the cost-efficiency of track inspections by targeting the available resources to high-risk locations. Unevenness of rail track running surfaces cause dynamic forces generated at the wheel/rail contacts which in return results the vibration of the car. This study focuses on detecting track unevenness by associating its influence on vibration with it. A comparative analysis is carried out on unevenness response prediction and the accuracy of detecting such track surface unevenness is analyzed with the ground truth location collected by the railroad track inspectors. The main finding of the study were 1) the unevenness event estimation error are within 15 matters with one run for one phone based system and 2) the three-phone based track surface anomaly detection system can improve its forecasting accuracy to 5 meters and to 3 meters with two traversals.

Measuring Acoustic Roughness of a Longitudinal Railhead Profile Using a Multi-Sensor Integration Technique.

It is necessary to measure accurately the rolling noise generated by the friction between wheels and rails in railway transport systems. Although many systems have recently been developed to measure the surface roughness of wheels and rails, there exist large deviations in measurements between each system whose measuring mechanism is based on a single sensor. To correct the structural problems in existing systems, we developed an automatic mobile measurement platform, named the Automatic Rail Checker (ARCer), which measures the acoustic roughness of a longitudinal railhead profile maintaining a constant speed. In addition, a new chord offset synchronization algorithm has been developed. This uses three displacement sensors to improve the measuring accuracy of the acoustic roughness of a longitudinal railhead profile, thereby minimizing the limitations of mobile platform measurement systems and measurement deviation. We then verified the accuracy of the measurement system and the algorithm through field tests on rails with different surface wear conditions.

A 3D Laser Profiling System for Rail Surface Defect Detection.

Rail surface defects such as the abrasion, scratch and peeling often cause damages to the train wheels and rail bearings. An efficient and accurate detection of rail defects is of vital importance for the safety of railway transportation. In the past few decades, automatic rail defect detection has been studied; however, most developed methods use optic-imaging techniques to collect the rail surface data and are still suffering from a high false recognition rate. In this paper, a novel 3D laser profiling system (3D-LPS) is proposed, which integrates a laser scanner, odometer, inertial measurement unit (IMU) and global position system (GPS) to capture the rail surface profile data. For automatic defect detection, first, the deviation between the measured profile and a standard rail model profile is computed for each laser-imaging profile, and the points with large deviations are marked as candidate defect points. Specifically, an adaptive iterative closest point (AICP) algorithm is proposed to register the point sets of the measured profile with the standard rail model profile, and the registration precision is improved to the sub-millimeter level. Second, all of the measured profiles are combined together to form the rail surface through a high-precision positioning process with the IMU, odometer and GPS data. Third, the candidate defect points are merged into candidate defect regions using the K-means clustering. At last, the candidate defect regions are classified by a decision tree classifier. Experimental results demonstrate the effectiveness of the proposed laser-profiling system in rail surface defect detection and classification.

DEPLOYMENT OF ERTMS IN CZECH REPUBLIC

The article describes current situation and expected deployment of international standard for automatic rail vehicle protection in Czech Republic for period 2014 – 2020. Article introduces expected status in 2020 and subsequent implementation of commitments until 2026. It introduces expected result of gradual implementation for tracks and vehicles. It also displays brief deployment overview of European Rail Traffic Management System in European Union countries for each level of European Train Control System.

동일 변위센서를 사용한 레일표면 음향조도의 측정방법

열차 운행시에 발생하는 전동소음은 열차 차륜과 레일 표면조도의 불균일성에 의하여 가진된 진동에 의해 발생된다. 따라서 열차 전동소음의 전달, 예측 및 분석을 위해서는 차륜과 레일표면의 음향조도(acoustic roughness)의 정확한 측정과 분석이 필요하다. 그러나 현재의 레일표면의 조도측정을 위한 장치와 방법은 작업자의 수작업에 의존한 트롤리 장치를 사용함으로써, 불일정한 측정속도와 흔들림과 같은 불안정한 인터페이스를 야기하여 측정값의 오차와 외란을 증대시키는 단점을 지닌다. 본 논문은 철도궤도의 레일상에서 자율 정속 주행하면서 레일표면의 음향조도를 측정하는 자동화 측정장치 및 플랫폼을 개발함으로써, 측정정밀도를 향상시키고 측정값의 불균일성을 감소시켰다. 또한 측정값의 신뢰도와 정확도 향상을 위해 동일한 복수의 변위센서 적용법을 제안하였으며 표준레일구간의 현장측정을 통해 동일 변위센서로부터 융합된 데이터의 정확성을 검증하였다. Rolling noises during train operation are caused by vibration excited from irregularities of surface roughness between wheel and rail. Therefore, a proper measurement and analysis techniques of acoustic roughness between wheel and rail surface are required for transmission, prediction, and analysis of the train rolling noise. However, since current measuring devices and methods use trolley-based manual handling devices, the measurements induce unstable measuring speed and vibrational interface that increases errors and disturbances. In this paper, a new automatic rail surface exploring platform with a speed controller has been developed for improving measurement accuracy and reducing inconsistency of measurements. In addition, we propose a data integration method of the rail surface roughness with multiple homogeneous displacement sensors and verified the accuracy of the integrated data through standard test-bed railway track investigation.

Anti Collision and Secured Level Crossing System

road and rail accidents are increasing with the advancement of human errors. Majority of the level crossing positions are not guided by any accident preventive mechanism and even not accompanied by a skilled person. This paper presents an automatic rail gate control system at level crossing positions and accident prevention mechanism. Two vibration sensors are used to control the open and close state of the gate at level crossing position. An ultrasonic sensor is positioned to detect an unauthorized object on the track. Open and Close status of the gate and unauthorized object on the track will be communicated with the central control room using wireless communication protocol. The experimental results proved the proposed mechanism is a prudent approach to safeguard the human and to curtail the train accidents.

Rail Component Detection, Optimization, and Assessment for Automatic Rail Track Inspection

In this paper, we present a real-time automatic vision-based rail inspection system, which performs inspections at 16 km/h with a frame rate of 20 fps. The system robustly detects important rail components such as ties, tie plates, and anchors, with high accuracy and efficiency. To achieve this goal, we first develop a set of image and video analytics and then propose a novel global optimization framework to combine evidence from multiple cameras, Global Positioning System, and distance measurement instrument to further improve the detection performance. Moreover, as the anchor is an important type of rail fastener, we have thus advanced the effort to detect anchor exceptions, which includes assessing the anchor conditions at the tie level and identifying anchor pattern exceptions at the compliance level. Quantitative analysis performed on a large video data set captured with different track and lighting conditions, as well as on a real-time field test, has demonstrated very encouraging performance on both rail component detection and anchor exception detection. Specifically, an average of 94.67% precision and 93% recall rate has been achieved for detecting all three rail components, and a 100% detection rate is achieved for compliance-level anchor exception with three false positives per hour. To our best knowledge, our system is the first to address and solve both component and exception detection problems in this rail inspection area.