Railway Monitoring Research Articles

A Deep Learning Based Method for Railway Overhead Wire Reconstruction from Airborne LiDAR Data

Automatically and accurately reconstructing the overhead wires of railway from airborne laser scanning (ALS) data are an efficient way of railway monitoring to ensure stable and safety transportation services. However, due to the complex structure of the overhead wires, it is challenging to extract these wires using the existing methods. This work proposes a workflow for railway overhead wire reconstruction using deep learning for wire identification collaborating with the RANdom SAmple Consensus (RANSAC) algorithm for wire reconstruction. First, data augmentation and ground points down-sampling are performed to facilitate the issues caused by insufficient and non-uniformity of LiDAR points. Then, a network incorporating with PointNet model is proposed to segment wires, pylons and ground points. The proposed network is composed of a Geometry Feature Extraction (GFE) module and a Neighborhood Information Aggregation (NIA) module. These two modules are introduced to encode and describe the local geometric features. Therefore, the capability of the model to discriminate geometric details is enhanced. Finally, a wire individualization and multi-wire fitting algorithm is proposed to reconstruct the overhead wires. A number of experiments are conducted using ALS point cloud data of railway scenarios. The results show that the accuracy and MIoU for wire identification are 96.89% and 82.56%, respectively, which demonstrates a better performance compared to the existing methods. The overall reconstruction accuracy is 96% over the study area. Furthermore, the presented strategy also demonstrated its applicability to high-voltage powerline scenarios.

ModelicaML modeling analysis of MCPS-based high-speed railroad safety monitoring system

Mobile Cyber-Physical System(MCPS) focuses on modeling of the physical world, dynamic continuity, real-time, spatial, security, and real-time predictable communication problems. Based on the example of high-speed railway system, this article mainly analyzes and studies the railway safety monitoring subsystem. Firstly, a brief analysis of the railway safety monitoring system and architecture is made, and the database connection pool and space-time analysis model are used as the software modeling system according to the requirements; then, SysML and Modelica are used to model the study, based on which a reconfigured modeling language called ModelicaML is proposed to model and simulate the modern engineering; lastly, the ModelicaML is used to physically model the safety detection system of high-speed railroad system, and finally, it is simulated and verified.

Railway traffic monitoring with trackside fiber-optic cable by distributed acoustic sensing Technology

The importance of railway safety cannot be overemphasized; hence it requires reliable traffic monitoring systems. Widespread trackside telecommunication fiber-optic cables can be suitably deployed in the form of dense vibration sensors using Distributed Acoustic Sensing technology (DAS). Train-induced ground motion signals are recorded as continuous “footprints” in the DAS recordings. As the DAS system records huge datasets, it is thus imperative to develop optimized/stable algorithms which can be used for accurate tracking of train position, speed, and the number of trains traversing the position of the DAS system. In this study, we transform a 6-days continuous DAS data sensed by a 2-km cable into time-velocity domain using beamforming on phase-squeezed signals and automatically extract the position and velocity information from the time-beampower curve. The results are manually checked and the types of the trains are identified by counting the peaks of the signals. By reducing the array aperture and moving subarrays, the train speed-curve/motion track is obtained with acceptable computational performance. Therefore, the efficiency and robustness of our approach, to continuously collect data, can play a supplementary role with conventional periodic and time-discrete monitoring systems, for instance, magnetic beacons, in railway traffic monitoring. In addition, our method can also be used to automatically slice time windows containing train-induced signals for seismic interferometry.

Structural health monitoring of railway bridges using innovative sensing technologies and machine learning algorithms: a concise review

Abstract Railway bridges are a vital element of railway infrastructures, and their safety can directly affect the regional economy and commuter transportation. However, railway bridges are often subjected to severe loading and working conditions, caused by rising traffic levels and heavier vehicles, and increases in train running speeds makes the bridges extremely susceptible to degradation and failure. One of the promising tools for evaluating the overall safety and reliability of railway bridges is the bridge structural health monitoring (SHM) system, which not only monitors the structural conditions of bridges and maintains the safety of train operations, but also helps to expand the lifespan of bridges by enhancing their durability and reliability. While a multitude of review papers on SHM and vibration-based structural damage detection methods have been published in the past two decades, there is a paucity of literature that provides a review or overview on the SHM of railway bridges. Some of the review papers have become obsolete and do not reflect the state-of-the-art research. Therefore, the main goal of this article is to summarize state-of-the-art SHM techniques and methods that have been widely used and popular in recent years. First, two state-of-the-art SHM sensing technologies (i.e. fiber optic sensing (FOS) technology and computer vision-based (CV) technology) are reviewed, including the working principles of various sensors and their practical applications for railway bridge monitoring. Second, two state-of-the-art machine learning algorithms (i.e. convolutional neural networks (CNN) and transfer learning (TL)) and their applications for railway bridge structural condition assessment are exemplified. Third, the principle of digital twin (DT) and its applications for railway bridge monitoring are presented. Finally, issues related to the future direction and challenges of the monitoring technologies and condition assessment methods of railway bridges are highlighted.

IOT BASED RAILWAY ACCIDENT PREVENTION AND MONITORING SYSTEM

The railways are the most common and flexible mode of transportation in the majority of the world's urban areas. The railway is a popular, inexpensive, and pleasurable mode of transportation. It is accessible to people from all walks of life. The rail transportation infrastructure is essential for travel in the modern day; nonetheless, numerous accidents occur on railway lines as a result of reckless driving, particularly during morning and evening rush hours, and pedestrians crossing while using mobile phones. To prevent railway accidents, a better technique must be invented, thus we created this project to tell pedestrians when a train is coming within a specific distance, so ensuring their safety and providing the security department with an alarm signal. The project entitled "IOT based Railway Accident Prevention and Monitoring System" is based on the Internet of Things and a live sensor database, which offers a method for preventing railway accidents. Its objective is to keep people safe by employing sirens to inform them of potential accidents at railroad grade crossings, to monitor any potentially hazardous conduct that might cause an accident at a railway grade crossing, and to provide the department and a person with an alert signal.

Research Progress in Distributed Acoustic Sensing Techniques.

Distributed acoustic sensing techniques based on Rayleigh scattering have been widely used in many applications due to their unique advantages, such as long-distance detection, high spatial resolution, and wide sensing bandwidth. In this paper, we provide a review of the recent advancements in distributed acoustic sensing techniques. The research progress and operation principles are systematically reviewed. The pivotal technologies and solutions applied to distributed acoustic sensing are introduced in terms of polarization fading, coherent fading, spatial resolution, frequency response, signal-to-noise ratio, and sensing distance. The applications of the distributed acoustic sensing are covered, including perimeter security, earthquake monitoring, energy exploration, underwater positioning, and railway monitoring. The potential developments of the distributed acoustic sensing techniques are also discussed.

Vibration compensation for railway track displacement monitoring system using biomedical image processing concept

Vibration compensation in track displacement monitoring is explored by using rail image feature points. The key point is to compare the image of the rail to be tested with the image of the standard rail. A downstream section of Metro Line A is selected for real time analysis of image. The sampling frequency of the detection system is 0.5m/point, and the driving speed of the detection vehicle is about 40km/h, take the distance of 100m between K23+000-K22+900 of kilometer mark, total 200 points, and analyze the image data by grey scale conversion concept. In order to verify the accuracy of the vibration compensation data, one point is taken at an interval of 5m, and a total of 20 points are measured, and compare and analyze with dynamic real-time detection data. After vibration compensation, the geometric parameters of the contact rail are closer to the expected value, and the maximum error can be controlled within ±3mm. Using biomedical image-based processing concept to realize the track displacement monitoring method, which meets the requirements of non-contact measurement, and the degree of automation is high, it can be monitored and alarmed in real time, and it has a good application prospect.

Fatigue Resistance of a BFRP-Encapsulated Long-Gauge FBG Strain Sensor under Cyclic Train Loads

To verify the performance of a basalt fiber reinforced polymer (BFRP) fiber-encapsulated long-gauge strain sensor in railway bridge health monitoring, this paper studies the fatigue resistance and durability of the BFRP fiber-encapsulated FBG sensor under train loads. First, the influences of the length of the anchorage section and the length ratio of the sensing section on the accuracy of the sensor were studied. Then, the BFRP sensor was applied to a sleeper for 2 million cycles of tension fatigue testing. The strain-time history of the whole fatigue test was monitored and compared. After the test, a calibration test was carried out to verify the accuracy and repeatability of the sensor. Finally, the slip and fatigue cracking of the fiber in the anchorage section of the sensor were observed by electron microscopy. The results show that the gap between the anchoring section and the bare optical fiber was filled with epoxy resin, and there was no slip behavior. No fatigue cracking occurred in the fiber, and the strain coefficient and linearity of the sensor showed no obvious changes after 2 million cycles of loading. The long-gauge strain sensor encapsulated by BFRP fibers exhibited good fatigue resistance and can meet long-term monitoring requirements under train loads.

Simultaneous Localization of Rail Vehicles and Mapping of Environment With Multiple LiDARs

Precise and real-time rail vehicle localization as well as railway environment monitoring is crucial for railroad safety. In this letter, we propose a multi-LiDAR based simultaneous localization and mapping (SLAM) system for railway applications. Our approach starts with measurements preprocessing to denoise and synchronize multiple LiDAR inputs. Different frame-to-frame registration methods are used according to the LiDAR placement. In addition, we leverage the plane constraints from extracted rail tracks to improve the system accuracy. The local map is further aligned with global map utilizing absolute position measurements. Considering the unavoidable metal abrasion and screw loosening, online extrinsic refinement is awakened for long-during operation. The proposed method is extensively verified on datasets gathered over 3000 km. The results demonstrate that the proposed system achieves accurate and robust localization together with effective mapping for large-scale environments. Our system has already been applied to a freight traffic railroad for monitoring tasks.

Real time read-frequency optimization for railway monitoring system

Trains have a key role in transporting people and goods with the option of moving from source to destinations by passing through several stations, with time-based features like date scheduling and known arrival times, which makes time a critical factor. The main challenge here, is to ensure that the train trip or train schedules are not affected or delayed in any way during the whole train trip; by giving the control unit in the railway system, the required time to process requests regarding all collected data. This an NP-hard problem with an optimal solution of handling all collected data and all service requests by the control unit of the railway system. Operational research will be used to solve this problem by developing many heuristics to deal with tasks of real-time systems, to produce a significant time optimization in the railway systems. To solve this problem, the proposed approach employs optimization by adapting 22 heuristics based on two categories of algorithms, the separated blocks category algorithm and the blocks interference category algorithm. The proposed approach receives data from many different sources at the same time, then collects the received data and save it to a data base in the railway system control unit. Experimental results showed the effectiveness of the developed heuristics, more over the proposed approach minimized the maximum completion time that was elapsed in handling the received requests.

EVALUATION OF PIXEL SELECTION METHODS FOR TRAFFIC INFRASTRUCTURE MONITORING USING SENTINEL-1 INSAR

Abstract. The Synthetic Aperture Radar (SAR) satellite Sentinel-1 provides excellent traffic infrastructure monitoring capabilities due to its short revisit time, wide-scale coverage and free of charge data policy. However, pixels from Sentinel-1 have a medium spatial resolution so that traffic infrastructure is only covered by a few pixels. Moreover, Interferometric Synthetic Aperture Radar (InSAR) yields deformation time series for coherent pixels only, thus limiting the number of pixels reporting the ground motion at traffic infrastructure. Although various InSAR time series methods have been successfully applied for traffic infrastructure monitoring, the selection of appropriate methods achieving a high pixel density has not yet been evaluated. In this study, we test whether we can improve the monitoring capabilities by combining different InSAR time series methods. On the one hand, we applied widely used Stanford Method for Persistent Scatterers (StaMPS) as a baseline method to retrieve the deformation time series. On the other hand, we enhanced the phase quality in a pre-processing step using the Phase-Linking (PL) approach and afterwards estimated the deformation time series also with StaMPS based on pixels selected by PL. We compared and evaluated the achieved pixel densities from both methods at main roads, highways and railways in a study area in Germany. We found that the InSAR time series methods selected complementary sets of pixels at all traffic infrastructure types. Moreover, the results indicate a great potential for railway monitoring using Sentinel-1 InSAR due to both high pixel density and homogeneous spatial distribution of pixels. Interestingly, coherent scatterers selected by StaMPS were observed to coincide with large traffic signs at highways which show a double-bounce scattering in the amplitude images. This study shows the benefits of combining PL and StaMPS for increased pixel density at traffic infrastructure and confirms Sentinel-1 data as a suitable data source for traffic infrastructure monitoring.

Condition Monitoring of Railway Crossing Geometry via Measured and Simulated Track Responses.

This paper presents methods for continuous condition monitoring of railway switches and crossings (S&C, turnout) via sleeper-mounted accelerometers at the crossing transition. The methods are developed from concurrently measured sleeper accelerations and scanned crossing geometries from six in situ crossing panels. These measurements combined with a multi-body simulation (MBS) model with a structural track model and implemented scanned crossing geometries are used to derive the link between the crossing geometry condition and the resulting track excitation. From this analysis, a crossing condition indicator is proposed. The indicator is defined as the root mean square (RMS) of a track response signal γ that has been band-passed between frequencies corresponding to track deformation wavelength bounds of and for the vehicle passing speed (f = v/). In this way, the indicator ignores the quasi-static track response with wavelengths predominantly above and targets the dynamic track response caused by the kinematic wheel-crossing interaction governed by the crossing geometry. For the studied crossing panels, the indicator ( and ) was evaluated for γ = u, v, or a as in displacements, velocities, and accelerations, respectively. It is shown that this condition indicator has a strong correlation with vertical wheel–rail contact forces that is sustained for various track conditions. Further, model calibrations were performed to measured sleeper displacements for the six investigated crossing panels. The calibrated models show (1) a good agreement between measured and simulated sleeper displacements for the lower frequency quasi-static track response and (2) improved agreement for the dynamic track response at higher frequencies. The calibration also improved the agreement between measurements and simulation for the crossing condition indicator demonstrating the value of model calibration for condition monitoring purposes.

Special Microstructured Optical Fiber Based Vibration Sensor and Its Application in Railway Monitoring

Special Microstructured Optical Fiber Based Vibration Sensor and Its Application in Railway Monitoring

Large Dynamic Range Optical Fiber Distributed Acoustic Sensing (DAS) With Differential-Unwrapping-Integral Algorithm

Optical fiber distributed acoustic sensing (DAS) based on phase sensitive optical time domain reflectometry (ϕ-OTDR) has a great demand in many application fields with large signals, such as railway safety monitoring, cable vibration detection, etc. However, due to the inherent limits of phase unwrapping algorithm, the large signal whose phase change exceeds π cannot be recovered correctly by DAS. In this work, the differential-unwrapping-integral (DUI) algorithm is proposed and demonstrated to replace the traditional unwrapping algorithm for large signal recovery. By utilizing differential operation to compress the signal amplitude, the large phase signal can be converted into small signal which can be recovered through unwrapping. Then, multiple integral operations and polynomial fitting compensation algorithm are applied onto the differential signal to recover the original phase signal without any signal-to-noise ratio(SNR) sacrificed. In theory, the maximum measurable phase of DUI can be improved 1390 times compared with traditional DUI algorithm. Further, the experimental results demonstrate that the dynamic range of DAS with DUI can reach up to 131.7 dB. Moreover, DUI algorithm has been tested and verified in the field test for recording the digging signals with large amplitude. This scheme can be applicable for all phase-demodulation-based sensing techniques without increasing the system complexity.

Improvement of autonomous railway monitoring robot prototype as fault detection on railway

One of the causes of accidents on trains to date has been caused by damage of railways such as cracked rails, broken rails and other rail defects. Currently, the inspection of the railway line is still carried out manually by the railway inspection officer or Petugas Penilik Jalan Rel (PPJ). However, there is a prototype Autonomous Railways Monitoring Robot (ARMR) which is a prototype inspection robot that can perform rail line checks automatically and be monitored remotely to detect any faults on the railway. The ARMR prototype detects faults as well as locations that are subsequently delivered to users by utilizing some Internet of Things (IoT) features. Faults detected by ARMR prototypes are faults as rail damage. But, the ARMR prototype is unable to distinguish a fault and rail connection. This final task aims to improve armr prototype capabilities to produce information in the form of depth and width of faults, and to distinguish faults and rail connections. The VL6180X sensor is a laser- based distance sensor used as a depth detector for rail faults and rail connections. The Incremental Encoder is enabled to measure the width of the rail fault. The result of ten experiments at 20 cm/s, the ARMR prototype was able to produce 78 mm fault depth size information with an error value of 1,67%, produce 5 mm wide fault width information with an error value of 2,48 % and be able to distinguish faults and connections with 72 % accuracy for the comparison of the distance between the ARMR prototype and the distance shown on google maps.

Enhanced densely dehazing network for single image haze removal under railway scenes

Purpose This paper aims to propose an enhanced densely dehazing network to suit railway scenes’ features and improve the visual quality degraded by haze and fog. Design/methodology/approach It is an end-to-end network based on DenseNet. The authors design enhanced dense blocks and fuse them in a pyramid pooling module for visual data’s local and global features. Multiple ablation studies have been conducted to show the effects of each module proposed in this paper. Findings The authors have compared dehazed results on real hazy images and railway hazy images of state-of-the-art dehazing networks with the dehazed results in data quality. Finally, an object-detection test is taken to judge the edge information preservation after haze removal. All results demonstrate that the proposed dehazing network performs better under railway scenes in detail. Originality/value This study provides a new method for image enhancing in the railway monitoring system.

A multi-scale image and dynamic candidate region-based automatic detection of foreign targets intruding the railway perimeter

This paper focuses on image characteristics of railway monitoring scenes, and proposed a multi-scale image and dynamic candidate region-based automatic detection of foreign targets intruding the railway perimeter. The algorithm first constructs a multi-scale image set for each frame, then obtains a dynamic candidate region of the intrusion target according to the law of gray projection curve variation for the low-resolution image. The foreground image, containing the intrusion target, is obtained using the background differential method for the high-resolution image, then the intruding target is identified before fusion of the results from the two methods. Three railway surveillance videos are used to validate the algorithm comparing with other methods, currently used in the railway scene. Overall, the proposed algorithm guarantees high-accuracy real-time detection of intruding targets, while requiring little computational resources, which is able to achieve 89.3% Precision and 0.028 s/frame with CPU. This technology can reduce the workload of manual inspections, and improve the detection accuracy through the improvement of the model can also reduce the manual review work caused by false alarms.

Railway Track Loss-of-Stiffness Detection Using Bogie Filtered Displacement Data Measured on a Passing Train

This paper presents an innovative numerical framework for railway track monitoring using acceleration measurements from sensors installed on a passenger train. A numerical model including a 10 degrees of freedom train passing over a three-layer track is employed. The bogie filtered displacement (BFD) is obtained from the bogie vertical acceleration using a numerical integration method and a band-pass filter. The BFD is compared to the filtered track longitudinal profile and can be seen to contain the main features of the track profile. This is also experimentally confirmed using field measurements where an in-service Irish Rail train was instrumented using inertial sensors. The proposed algorithm is employed to find the BFDs from the bogie accelerations. A track level survey was also undertaken to validate the measurements. It is shown that the BFDs from several passes are in good agreement with the surveyed profile. Finally, the BFDs are numerically used to find track defects such as hanging sleepers. The mean of the BFDs obtained from two populations of train passes over a healthy and a damaged track are employed to detect the loss of stiffness at the subgrade layer. The effect of the train forward speed variation and measurement noise are also investigated.

Sponge-Supported Triboelectric Nanogenerator for Energy Harvesting from Rail Vibration

Smart health monitoring based on a wireless sensor network plays a crucial role in maintaining the efficiency and safety of railway systems during operation, while the power source of the railway monitoring system remains a problem. Considering the mechanical energy produced by the rail vibration to be a sustainable and environment friendly power source, we propose a sponge-supported triboelectric nanogenerator (S-TENG) device as an energy harvester for the railway monitoring system. A high-density sponge is utilized under the tribo-pair as an innovative flexible buffer structure. The initial gap between the tribo-pair is properly designed according to the rail vibration amplitude to guarantee that the two dielectric materials can get into contact, while the rail with displacement beyond the initial gap will compress the sponge instead of squashing the tribo-pair, so as not to break the tribo-pair and to improve the output performance by enhancing the contact force between the tribo-pair. The feasibility of the sponge base is proved through a mechanical test, and a series of well-designed experimental tests are carried out to study the output performance of S-TENG with various loading conditions. To further investigate the applicability of S-TENG in railway monitoring, we establish a theoretical model for S-TENG output performance combined with a metro train-floating slab track model and simulate the output characteristics of S-TENG under train operation.

WSN Topology Control algorithm for High Speed Railway Monitoring

In order to solve the safety problems caused by the continuous improvement of train speed and the continuous growth of railway scale, it is inevitable to apply wireless sensor network (WSN) to train operation environment monitoring. In order to prolong the survival time of wireless monitoring network, the game theory is used to construct a distributed topology model. In order to solve the problem of network lifetime shortening caused by uneven energy consumption of nodes, the Theil index is introduced to improve the energy consumption balance of nodes, and a topology control algorithm for energy consumption balance (EBTCA) is proposed. Experiments show that the algorithm has good results in terms of network lifetime and energy balance.