Rail Monitoring Research Articles

Rail crack identification of in-service turnout through Bayesian inference

In-service railway turnout, suffering from multi-factor coupling of operational and environmental loadings and special wheel–rail interaction, is prone to structural damage. The paper aims to develop a Bayesian damage identification method for crack-alike damage of the turnout rail under uncertainties. It consists of three core parts, including a damage index (DI) constructed by the transformation of time–frequency components of responses for generating damage-sensitive relationships, Bayesian models describing the crack-sensitive relationships hidden in the members of the damage index, and a mechanism synthesizing individual assessment results associated with the different reference models for providing one quantitative solution. The abnormal change in the stable relationships derived from the index can reflect damage occurrence and severity. The Bayesian approach is adopted to model the relationships under uncertainties of in-service railway turnout. The models trained by using monitoring data of the turnout rail before being damaged serve as a reference for healthy state, and deviations of actual observations from model predictions may indicate the existence of damage. The synthesizing process helps to offer a more rational assessment result through the weighted summation of individual quantitative assessment results. Rail monitoring data of a railway turnout are acquired to examine the damage detection performance of the proposed method. By exempting loadings measurement and physical model derivation, this data-driven methodology is potentially capable of supporting the damage identification and quantitative assessment of other structures in railway engineering.

INCREASING THE RELIABILITY OF HIGH-SPEED RAIL TESTING BY THE BOTTOM-ECHO METHOD

The current regulatory documents do not take into account the peculiarities of rail monitoring by mobile flaw-detectors, are focused on compliance with the initially set parameters and cannot always provide the required reliability of defect detection. The issues of maintaining the required reliability of ultrasonic inspection under the influence of interfering factors at significant scanning speeds by adjusting the threshold level of defect detection are considered. When implementing the bottom-echo method of ultrasonic inspection, an informative feature is the amplitude of the bottom signal. When analyzing the envelope of the bottom signal amplitude, an important role is played by establishing a baseline level from which the magnitude of the amplitude decrease is calculated. To automate the process of decoding ultrasonic data of mobile flaw-detectors, a special algorithm for estimating the baseline level of the bottom signal has been developed, implemented in the corresponding software for automatic processing of acoustic inspection data.

Rail Structural Health Monitoring using ultrasonic guided waves: system design and deployment

Railways are a central industrial infrastructure enabling people and goods transportation all across the globe and their use is due to increase in the forthcoming years to reduce the overall carbon footprint of our modern societies. Hence the need to ensure their reliable use, as traffic interruptions or accidents can have dire social, economic and environmental impacts. In particular, rail breaks can produce such incidents and need to be monitored and prevented if possible. Ultrasonic guided waves are a promising physical phenomenon to monitor rails as they can enable the monitoring of long portions with a limited number of sensors. This talk will present a SHM system relying on such waves for rail monitoring which has been deployed for industrial tests on a subway line. The electronics, the sensors and their coupling to the rail were optimized to ensure the measurement of guided waves after a long-range propagation throughout the life of the system. Indeed, specific couplings are needed to enable long-time coupling, and so ultrasound sensitivity of the system, while the rails are bearing high loads or dilating due to temperature changes. An experimental campaign was carried out to evaluate the sensitivity of the system to rail breaks, and to demonstrate its detection under representative, uncontrolled environmental conditions. Particular considerations linked to pulse-echo inspections, inducing a sensitivity to cut-off frequencies and zero-group velocity modes, will be presented.

Study on monitoring broken rails of heavy haul railway based on ultrasonic guided wave

Real-time monitoring of broken rails in heavy haul railways is crucial for ensuring the safe operation of railway lines. U78CrV steel is a common material used for heavy haul line rails in China. In this study, the semi-analytical finite element (SAFE) method is employed to calculate the dispersion curves and modal shapes of ultrasonic guided waves in U78CrV heavy steel rails. Guided wave modes that are suitable for detecting rail cracks across the entire cross-section are selected based on the total energy of each mode and the vibration energy in the rail head, web, and foot. The excitation method for the chosen mode is determined by analyzing the energy distribution of the mode shape on the rail surface. The ultrasonic guided wave (UGW) signal in the rail is analyzed using ANSYS three-dimensional finite element simulation. The group velocity of the primary mode in the guided wave signal is obtained through continuous wavelet transform to confirm the existence of the selected mode. It is validated that the selected mode can be excited by examining the similarity between the vibration shapes of a specific rail section and all modal vibration shapes obtained through SAFE. Through simulation and field verification, the guided wave mode selected and excited in this study demonstrates good sensitivity to cracks at the rail head, web, and foot, and it can propagate over distances exceeding 1 km in the rail. By detecting the reflected signal of the selected mode or the degree of attenuation of the transmitted wave, long-distance monitoring of broken rails in heavy-haul railway tracks can be achieved.

Deep learning techniques to detect rail indications from ultrasonic data for automated rail monitoring and maintenance

The increasing number of passengers and services using railways and the corresponding increase in rail use has caused the acceleration of rail wear and surface defects which makes rail defect identification an important issue for rail maintenance and monitoring to ensure safe and efficient operation. Traditional visual inspection methods for identifying rail defects are time-consuming, less accurate, and associated with human errors. Deep learning has been used to improve railway maintenance and monitoring tasks. This study aims to develop a structured model for detecting railway artifacts and defects by comparing different deep-learning models using ultrasonic image data. This research showed whether it is practical to identify rail indications using image classification and object detection techniques from ultrasonic data and which model performs better among the above-mentioned methods. The methodology includes data processing, labeling, and using different conventional neural networks to develop the model for both image classification and object detection. The results of CNNs for image classification, and YOLOv5 for object detection show 98%, and 99% accuracy respectively. These models can identify rail artifacts efficiently and accurately in real-life scenarios, which can improve automated railway infrastructure monitoring and maintenance.

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.

Heterogeneity‐oriented ensemble learning for rail monitoring based on vehicle‐body vibration

AbstractIn this study, railway vehicle‐body vibration was applied to rail detection for convenient sensor deployment and cost‐effectiveness. However, the waveform is difficult to analyze due to damping and interference. Data‐driven methods can help concatenate multidimensional signals and complex rail‐surface irregularities but are impressionably uncertain. This study proposes a method in which a deep learning framework is coupled with heterogeneous factors at every link in its ensemble strategy. The instantiation, module foundation, and scenario description establish a concrete system for dealing with the dilemma that an insufficient database hinders feature extraction, causing unproductive capacity. The performance is quantitatively discussed by combining different levels of on‐site rail‐surface conditions, reaching prediction errors of 4.7% and 6.5% and classifier accuracies of 98.4% and 93.7% for irregularities and defect severities, respectively. This work describes a way to extend self‐learner applicability in industry and will facilitate new support for railway track management.

Output-only estimation of lateral wheel-rail contact forces and track irregularities

The contact between the wheel and the rail heavily affects the dynamic behaviour of railway vehicles and their running safety. The real-time knowledge of wheel-rail contact forces is tricky due to the several running conditions of the train and the combined tribological and wear characteristics of wheels and rails. Furthermore, their measurement and that of track irregularities, sensitive to the harshness of the wheel-rail contact, require dedicated sensors. Therefore, the development of strategies for estimating wheel-rail contact features is essential to perform the condition monitoring of rails to improve the safety related to the entire railway system. A nonlinear model-based estimation procedure, based on a Central Difference Kalman Filter, is proposed in this work to estimate the lateral wheel-rail contact forces and moments, including the identification of lateral track irregularities in the estimation process related to rails. Furthermore, the developed estimation technique can be used as a tool to monitor continuously the track conditions through in-service railway vehicles, coupled with the possibility to improve the effectiveness of control systems onboard them by estimating wheel-rail contact interactions through a model-based estimator, which is fed by measurements obtained by the typical set of sensors adopted for condition monitoring purposes on railway vehicles.

Ultrasonic SH guided wave detection of the defects of switch rails with 3D finite element method

A switch rail is an important part of the high-speed railway system, but it is highly susceptible to defects during service, which threat traffic safety. A switch rail has an asymmetric variable cross-section structure, which makes it challenging to detect its defects with ultrasonic testing. In this study, the finite element model of switch rails and the ultrasonic SH guided wave detection of groove defects were explored. The three-dimensional finite element model of switch rails with the same sizes used under working conditions was established. Furthermore, the influences of grid division method, switch mechanism setting method, and root end variable cross-section grid type on guided wave propagation in the model were analyzed in detail. The switch rail model was experimentally validated. The ultrasonic SH guided wave detection of the defects at the rail head and rail bottom was successfully realized and the echo signals from other special structures of switch rail were also obtained. The finite element model calibrated with experimental results could be used to simulate guided wave detection at any location with any defect. The calibrated model could optimize the switch rail guided wave detection scheme and guide signal identification. The study provides an important basis for the inspection and monitoring of switch rails.

Development of DMPS-EMAT for Long-Distance Monitoring of Broken Rail

The safety of railway transportation is crucial to social and economic development. Therefore, real-time monitoring of the rail is particularly necessary. The current track circuit structure is complex and costly, posing challenges to monitoring broken tracks using alternative methods. As a non-contact detection technology with a lower environmental impact, electromagnetic ultrasonic transducers (EMATs) have become a concern. However, traditional EMATs have problems such as low conversion efficiency and complex modes, which can limit their effectiveness for long-distance monitoring. Therefore, this study introduces a novel dual-magnet phase-stacked EMAT (DMPS-EMAT) design comprising two magnets and a dual-layer winding coil arrangement. The magnets are positioned at a distance equal to the wavelength of the A0 wave from each other, while the center distance between the two sets of coils beneath the transducer is also equal to the wavelength. After analyzing the dispersion curves of the rail waist, it was determined that the optimal frequency for long-distance rail monitoring is 35 kHz. At this frequency, adjusting the relative positions of the two magnets and the coil directly underneath to be one A0 wavelength can effectively excite a constructive interference A0 wave in the rail waist. The simulation and experimental results show that DMPS-EMAT excited a single-mode A0 wave, resulting in a 1.35-times increase in amplitude.

КОНТРОЛЬ ЦЕЛОСТНОСТИ РЕЛЬСОВЫХ НИТЕЙ ПРОТЕКАЮЩИМ ТОКОМ

Violations of the integrity of rails on railway sections pose a serious threat to traffic safety, cause accidents and wrecks, lead to significant economic damage and human casualties. The integrity of rails is controlled by flaw detection methods, as well as by a special mode of operation of rail circuits due to the signal current flow around track circuits. The widespread use of AC rail circuits which reliably perform the control mode made it possible to organize a simple method of integrity control over a significant length of railway lines. The complexity of maintenance and low reliability of track circuits, in turn, led to emergence of alternative technical solutions for monitoring the vacancy of railway sections. However, these solutions are not able to control the integrity of rails and prevent elimination of track circuits. On the other hand, the joint use of continuous monitoring of rails and low-maintenance solutions for monitoring the vacancy of track sections will ensure reduction in operating costs with a constant level of traffic safety. The article analyzes technical solutions for monitoring the integrity of rails due to continuous current flow. The maximum length of the contour formed by two rails is set to control the integrity of the rails. The prospects of solutions for monitoring the integrity of rails based on the electrical circuit formed by rails and a cable running along them are shown.

King Rail ( Rallus elegans ) response to audio playback: implications for population estimation, monitoring methodology, and trapping approach

Conspecific audio can be broadcast to improve detection probability (detectability) of secretive marsh bird species for population monitoring purposes and as a lure to more effectively trap individuals. Our primary objective was to describe King Rail (<em>Rallus elegans</em>) detectability as a function of distance and so determine if the distance sampling assumption of perfect detectability immediately adjacent to survey points was violated during call-broadcast surveys. We also described what factors affected King Rail detectability during audio broadcast surveys, and how and when this species was detected, and compared effectiveness of various King Rail call recordings used during trapping attempts. To accomplish these objectives, we experimentalized trapping efforts, radio-tagged 12 King Rails, and conducted repeated call-broadcast surveys for each radio-tagged individual in Michigan and Ohio, USA during the 2020 and 2021 breeding seasons. We found that King Rail detectability during 81 surveys was 0.39, which violates the aforementioned distance sampling assumption and necessitates a correction factor. Detectability within survey segments varied among survey periods, though overall detectability remained relatively constant, even after call-broadcast surveys in our study area typically end (post-survey period: 15 June to 10 July), which indicates that King Rail monitoring could effectively continue later than currently occurs. We found that grunt calls resulted in more captures than kek calls and that a multi-call track was most effective at trapping King Rails. Our findings could improve King Rail population estimates and increase success during trapping efforts, and may inform similar efforts for other secretive marsh birds.

Combined Acoustic Emission and Digital Image Correlation for Early Detection and Measurement of Fatigue Cracks in Rails and Train Parts under Dynamic Loading

Fatigue crack in rails and cyclic-loaded train parts is a contributory factor in multiple railroad accidents. We address the problem of crack detection and measurement at early stages, when total failure has not yet occurred. We propose to combine acoustic emission (AE) testing for prediction of crack growth with digital image correlation (DIC) for its accurate quantitative characterization. In this study, we imitated fatigue crack appearance and growth in samples of railway rail and two train parts by cyclic loading, and applied these two techniques for inspection. Experimental results clearly indicate the efficiency of AE in the early detection of fatigue cracks, and excellent DIC capabilities in terms of geometrical measurements. Combination of these techniques reveals a promising basis for real-time and non-destructive monitoring of rails and train parts.

An efficient approach for guided wave structural monitoring of switch rails via deep convolutional neural network-based transfer learning

Data-driven approaches in structural health monitoring have received increasing attention, especially advances in deep learning-based methods, which have further driven the development of data-driven damage detection. Due to the limited availability of guided wave samples and the imbalance between data classes, this study proposes a deep convolutional neural network-based transfer learning (DCTL) approach for the structure monitoring of switch rails using guided wave monitoring signals. A pretrained model based on Inception-ResNet-V2 was adopted and fine-tuned. Different methods for converting 1D signals into 2D images were investigated to find the optimal approach that meets practical monitoring requirements. Affine transformations were used for data augmentation to improve generalization ability and to avoid the overfitting of the training model. Two types of guided wave monitoring experiments on the foot and web of switch rails were conducted to evaluate the proposed method against different conventional methods in the field of switch rails. In addition, the DCTL method was investigated, with and without pretrained weights, along with different frozen layers. The classification results show that the proposed method can identify damage in challenging situations and outperforms conventional methods.

Sliding Window Dynamic Time-Series Warping-Based Ultrasonic Guided Wave Temperature Compensation and Defect Monitoring Method for Turnout Rail Foot.

Temperature changes are a major challenge in outdoor guided wave structural health monitoring of rails. Temperature variations greatly impact the waveform of guided wave signals, making it challenging to diagnose and characterize defects. Traditional temperature compensation methods, such as signal stretch and scale transform, are restricted to use in regular structures, such as plates and pipes. To solve the temperature compensation problem in long rails with serious mode conversion and complex structure echo, we propose a temperature compensation and defect monitoring method, namely, sliding window dynamic time-series warping (SWDTW), which overcomes the challenges of mass computation and overcompensation of dynamic time-series warping (DTW). The basic idea of SWDTW is to utilize sliding windows to accelerate the computation and identify defects from subsequence scales. Then, an index, window subsequence Teager energy (WSTE), is used to indicate the local abnormality of guided wave signals, and a sliding window net (SWnet) is devised to monitor the occurrence of defects automatically. Outdoor monitoring of turnout rails showed that the proposed method can effectively reduce the temperature noise and recognize an artificial defect with 1.16% and 0.36% cross-sectional change rates (CSCRs) on the switch and stock rails, respectively, at different temperatures; moreover, the defect signals processed by SWDTW showed better defect identification performance than those processed by scale transform and DTW.

Design of the receiving unit of the ultrasonic rail flaw detector

The development of an ultrasonic fl aw detector receiving unit for rail monitoring includes theoretical and practical studies. Special attention is paid to the experimental determination of the characteristics of the ultrasonic piezoelectric sensor and the description of the receiving-amplifying path. A mock-up sample of an ultrasonic fl aw detector allows checking rails for defects and gives a tangible gain in dimensions compared to bulky analogues.The theoretical methods used were based on the principles of electrical engineering, electronic engineering, graphical analysis and the theory of automatic control.Experimental studies of the piezoelectric sensor allowed us to determine its main resonant frequency. The correct choice of parameters of the amplifier unit ensured accurate and reliable operation of the device. Practical tests of the fl aw detector using a special buffer sample of the rail made it possible to adjust its sensitivityThe input amplifier of the microvoltmeter V3-40 is taken as the basis of the receiving and amplifying path of the fl aw detector. The device includes the possibility of its use in conjunction with the converter. The structural diagram of the fl aw detector is presented. The electrical circuit of the input divider with the selection of the necessary resistances and the electrical circuit of the effective value meter with a description of all its elements are described.Operating modes and electronic circuits are selected according to reference data, taking into account their high reliability of operation. The use of analog integrated circuits in the circuit solution made it possible to increase the reliability and accuracy of the fl aw detector device, simplify its adjustment, reduce zero drift and improve the quality of the output signal.

An in-depth assessment of convolutional neural networks for rail surface defect detection

The consistent monitoring of rails is based on correctly identifying defects to support corrective measures. Recently, convolutional neural networks (CNN), a deep learning method, have been providing outstanding results for the automatic detection of defects. However, several aspects of CNN-based approaches such as network architecture, transfer learning and processing time remains not fully understood. In this work, we performed an in-depth assessment of ten widely used CNN models with the objective of finding the one with the best performance in identifying defects in rail surface images. The classification results are promising, reaching an average accuracy of 83.7% on detection of mild defects and squat. The Inceptionv3 network provided the best results by correctly identifying 92% of images with severe squat defects.

Rail Tracking and Detection with Drone in Gazebo Environment

Rail transport is considered one of the safest modes of transport. Along with the development of technology, significant developments have been observed in railway transportation over the years. With the increasing railway line, the demand for railway transportation is increasing day by day. The number of passengers using railway transportation has also increased in this context. With this intensity, the damage to the railway line increases. In ensuring the safety of railway transport, methods based on deep learning have become important to ensure railway safety. In order for the railway line to provide healthy service, the monitoring of the railway line should be done regularly. Traditional rail monitoring services require different vehicles. Today, besides conventional vehicles, drones are used for the monitoring of the railway line. Experimenting with drones in the real environment can be difficult and costly. It is always more advantageous to run the codes that will run on the drone first in a simulation environment to save time. In this study, a drone-based system that autonomously tracks and detects railway tracks is proposed as an alternative to traditional methods. The proposed method detects the rails with the semantic segmentation method and follows the rails with its front camera. The proposed method was developed in the Gazebo environment. The general purpose of the study is to record the rail images with the drone camera that follows the railway autonomously. In this way, drone experiments to be carried out in the real railway environment will be completed in a shorter time.

An improved reconstruction method based on auto-adjustable step size sparsity adaptive matching pursuit and adaptive modular dictionary update for acoustic emission signals of rails

Compressed Sensing (CS) is an effective method for improving the real-time performance of crack-induced acoustic emission (AE) signals analysis in Structural Health Monitoring (SHM) of rails. Aiming to promote the reconstruction accuracy and speed of CS, a reconstruction method is proposed based on improved Sparsity Adaptive Matching Pursuit (SAMP) and modular dictionary update. In the proposed method, a multiscale-modular dictionary is devised based on a multiscale dataset to enhance the real-time performance of reconstruction. Meanwhile, the step size of the SAMP is adaptively adjusted by the kurtosis residuals, which promotes the reconstruction accuracy. Furthermore, to optimize the adaptability of the dictionary, kurtosis-deviation is utilized to update the dictionary adaptively and modularly. The proposed method was verified by tensile tests. The results demonstrate that the proposed method has a higher reconstruction accuracy and speed than other methods, which can guide real-time crack-induced signal analysis in the SHM of rails.

Carbon and Metallic-based Nanomaterials for Strain Sensors - A Review

Strain gauges are devices whose electrical resistances vary proportionately with the amount of strain applied on the device. They can be used for real-time applications in the aerospace sector, as a geotechnical tool in tunnels and bridges, in rail monitoring and health monitoring sectors. Nanomaterials have been widely used for this application because they can be flexible, stretchable and have high strength. Several researchers have used numerous carbon-based and metallic nanostructures to develop functionally graded materials. Among carbon-based materials, graphene has been widely researched as a viable material for strain sensors due to its superior mechanical and electrical properties. Also, many metallic nanoparticles have been investigated to design strain sensors that are highly sensitive to a wide range of strains. In this article, a review of carbon and metallic nanomaterial-based strain sensors is presented, with emphasis on applications pertaining to structural health monitoring and wearable devices.