Rail Defects Research Articles

Optimal Transport Theory for Railway Track Safety Applications

Abstract Optimal mass transport (OMT) is an optimization problem that determines a method of moving mass or value from one function to another in the most efficiently and optimally. Using the ideas of OMT, this technical note aims to formulate and solve an OMT model for railway track safety. The inputs for the analysis include (a) geometry data, (b) rail defect, (c) subsurface condition, and (d) tonnage. The technical note only presents the steps in achieving a complete OMT analysis. Therefore, the idea of the note is to serve as an illustration of the effectiveness of OMT in railway engineering applications.

Automated Identification and Localization of Rail Internal Defects Based on Object Detection Networks

The timely identification of rail internal defects and the application of corresponding preventive measures would greatly reduce catastrophic failures, such as rail breakage. Ultrasonic rail defect detection is the current mainstream rail defect detection method thanks to its advantages of strong penetration, high accuracy, and ease to deploy. The 2D B-scan image output by ultrasonic detectors contains rich features of defects; however, rail engineers manually identify and localize the defect image, which can be time-consuming, and the image may be subject to missing identification or mistakes. This paper adopted state-of-the-art deep learning algorithms for novel B-scan images for the automatic identification and localization of rail internal tracks. First, through image pre-processing of classification, denoising, and augmentation, four categories of defect image datasets were established, namely crescent-shaped fatigue cracks, fishbolt hole cracks, rail web cracks, and rail base transverse cracks; then, four representatives of deep learning object detection networks, YOLOv8, YOLOv5, DETR, and Faster R-CNN, were trained with the defects dataset and further applied to the testing dataset for defect identification; finally, the performances of the three detection networks were compared and evaluated at the data level, the network structure level, and the interference adaptability level, respectively. The results show that the YOLOv8 network can effectively classify and localize four categories of internal rail defects in B-scan images with a 93.3% mean average precision at three images per second, and the detection time is 58.9%, 376.8%, and 123.2% faster than YOLO v5, DETR, and Faster R-CNN, respectively. The proposed approach could ensure the real-time, accurate, and efficient detection and analysis of internal defects to a rail.

High-speed rail defect detection using multi-frequency exciting 3D ACFM

High load wheel-rail friction leads to surface defects on the rail, which requires an accuracy non-destructive testing (NDT) technology to prevent rail fracture and even major safety accidents. Alternating current field measurement (ACFM) has attracted attention due to its high detection precision and low sensitivity of lift-off and velocity effects. The paper proposes a multi-frequency, three-dimensional (3D) ACFM detection system that can obtain abundant detection information for multi-frequency comprehensive evaluation to improve detection accuracy. The system mainly includes an expandable phase-locked amplifier module and an adjustable multi-frequency signal source module, which provides a hardware IQ demodulation acceleration platform for multi-frequency and multi-channel ACFM detection to obtain enough defect signals for analysis. Analysis of the speed effect indicates that increasing the excitation frequency can improve the stability of the detection signal. We design a quantitative evaluation model for defects with a depth of 2–8 mm and surface angle between 30° and 75°.

Enhancing RCF rail defect inspection on the Serbian railway network

The interaction between the wheel and the running rails within the railway system introduces intricate stress patterns, resulting in the formation of rolling contact fatigue (RCF) rail defects. The magnitude of this stress is contingent upon factors such as track performance, vehicle characteristics, and service conditions. While advancements in rail metallurgy can mitigate the issue to some extent, no economically viable steel composition currently exists that can completely withstand the repetitive stresses associated with RCF. It is more cost-effective to properly maintain rails for longer use rather than replace them entirely. The paper emphasizes the importance of classifying and coding RCF rail defects in light of their potential adverse effects on rail transport safety. It provides an analysis of the available inspection methods for RCF rail defects and recommends the ones that should be implemented on the Serbian railway network. A combination of proposed inspection methods is preferred to increase detection efficiency for different types of RCF defects.

Rail Flaw Imaging Prototype Based on Improved Ultrasonic Synthetic Aperture Focus Method

This paper presents an experimental prototype developed for rail flaw imaging. This capability can help obtain quantitative information on detected flaws during manual flaw verification. Ultrasonic synthetic aperture focus (SAF) imaging has advantages over phased-array imaging for both speed and accuracy. The prototype developed is hosted in a portable and battery-powered carry-on size case. The probe is a linear ultrasonic array mounted on a wedge and with a position encoder to build 3D point clouds from 2D beamformed images. The prototype includes several advances over the basic SAF technique, including sparse subarray firing that allows fast imaging speeds (e.g., 25 Hz) without sacrificing image accuracy. Validation results are presented from scans performed on rail sections from the FRA rail defect library, which contains natural transverse defects and artificial end-drilled hole defects. The tests showed good accuracy in defect size and shape, as compared to the available ground truth information, for defects located away from the railhead corners. Additional developments are required to properly cover the head corners, and especially in the case of heavily worn rails.

Image segmentation method of rail head defects and area measurement of selected segments

The operation safety of railway transport, which is the most important economic and social factor, is largely determined by the technical condition of the rail track and measures to maintain the quality of its track management system. One of the system elements for ensuring the accident-free operation of the track is the technical diagnosis of rails using a method complex of non-destructive control of rails, such as acoustic (ultrasonic), magnetic, combined, etc., and monitoring of the track using methods of measuring the geometry of the rail track and its disturbances. When the wheel interacts with the rail, especially on high-speed and load-stressed sections, defects and damage inevitably occur in the rails. A rather large share of such defects are on the rolling surface of the rail head. Formed defects develop rapidly, which seriously complicates the safety of train traffic. Therefore, accurate and quick detection of defects on the rolling surface of the rail head is very important. However, it is quite difficult to detect defects on the rolling surface of the rail by the acoustic (ultrasound) method due to the violation of tight contact between the rolling surface of the rail head and the piezoelectric transducer. In this case, it is quite convenient to detect surface defects of the rail head using video control. The article provides a comparative analysis of segmentation methods. There has been presented the method of image segmentation of main rail defects based on general contour preparation and parallel-hierarchical (PH) transformation using their classification. The parallel-hierarchical transformation method allows to increase the segmentation accuracy of individual areas in the original image compared to similar ones. The algorithm of pyramidal generalized-contour preparation and the criterion system allows, by calculating the threshold for each level of the gray scale, to present the study of the image with the corresponding contour preparations at the segmentation level. Modeling of recursive generalized-contour preparation and PH transformation method for image segmentation problem of rail head defects shows that, compared to the segmentation method based on the increase of areas, the accuracy of image segmentation is better. A modified method of calculating the image contour area based on the coding of lines forming the boundaries of the black and white areas of the two-gradation image has been given.

RDNet-KD: Recursive Encoder, Bimodal Screening Fusion, and Knowledge Distillation Network for Rail Defect Detection

RDNet-KD: Recursive Encoder, Bimodal Screening Fusion, and Knowledge Distillation Network for Rail Defect Detection

EHA-YOLOv5: An Efficient and Highly Accurate Improved YOLOv5 Model for Workshop Bearing Rail Defect Detection Application

EHA-YOLOv5: An Efficient and Highly Accurate Improved YOLOv5 Model for Workshop Bearing Rail Defect Detection Application

Nonlinear autoregression-based non-destructive evaluation approach for railway tracks using an ultrasonic fiber bragg grating array

The safe operation of railway systems relies on rapid and accurate non-destructive damage detection and evaluation techniques to help rule out potential threats brought by rail track damages. In this paper, an innovative fiber Bragg grating (FBG) array-based sensing system is developed for rail defect inspection by integrating the ultrasonic testing technology, and a nonlinear autoregressive neural network with exogenous inputs (NARX) is proposed for rail damage detection based on measured ultrasonic signals. The developed sensing system can realize the high-speed and multiplexed ultrasonic signals demodulation. The NARX is proposed to process the ultrasonic signals obtained by the developed sensing system, and a specified probabilistic damage sensitive feature (DSF) is tailored to indicate rail health status, derived from the probability density function (PDF) of NARX prediction errors. To verify the proposed sensing system and damage identification algorithm, experimental studies considering four rail conditions (i.e., intact, cracked, bump, and welded conditions) are conducted in this study. First, baseline NARX models are priorly trained using ultrasonic signals acquired on an intact rail segment; then, DSFs corresponding to each condition are calculated enabling effective rail damage diagnosis. To optimize the training parameters of NARX models, three different training functions are utilized to select the most appropriate parameters that are sensitive to rail damages. Last, large-scale testing is conducted, further proving the applicability and reliability of the proposed rail damage evaluation approach. Through this study, it is suggested that integrating high-performance sensing technologies with effective data processing tools would maximumly enable efficient and robust non-destructive testing, especially on important infrastructures such as rails.

Resolving mode mixing in wheel–rail surface defect detection using EMD based on binary time scale

Due to the mode mixing, empirical mode decomposition (EMD) cannot effectively decompose the vibration signal when the signal is intermittent and pulse interference caused by discontinuous vibration. The methods to solve mode mixing often use noise assistance, such as ensemble EMD (EEMD), complete EEMD (CEEMD), etc. These methods can effectively solve mode mixing, but they also have shortcomings. In EEMD, the added noises not only have residual effects and time-consuming. The drawback of CEEMD is that it is difficult to align during set averaging. In this paper, an improved EMD based on binary time scale (EMD-BTS) is proposed for the fault feature extraction of wheel–rail defect detection. Firstly, the generalized intrinsic mode function (GIMF) is defined based on the time-domain characteristics of non-stationary vibration signals. Then, to tackle the drawbacks of EMD which cannot effectively solve mode mixing caused by signal intermittence and pulse interference, the inherent mode is extracted in the EMD-BTS to decompose the raw signals into GIMFs. Finally, the false components generated by over decomposition are combined based on time-domain cross covariance. A simulation case and a actual case of vehicle bogie are utilized to verify the feasibility of the proposed EMD-BTS. The results indicate that the proposed approach exceeds other typical techniques in extracting intermittent fault features of wheel–rail defect detection.

Rail Internal Defect Detection Method Based on Enhanced Network Structure and Module Design Using Ultrasonic Images

Improving the detection accuracy of rail internal defects and the generalization ability of detection models are not only the main problems in the field of defect detection but also the key to ensuring the safe operation of high-speed trains. For this reason, a rail internal defect detection method based on an enhanced network structure and module design using ultrasonic images is proposed in this paper. First, a data augmentation method was used to extend the existing image dataset to obtain appropriate image samples. Second, an enhanced network structure was designed to make full use of the high-level and low-level feature information in the image, which improved the accuracy of defect detection. Subsequently, to optimize the detection performance of the proposed model, the Mish activation function was used to design the block module of the feature extraction network. Finally, the proposed rail defect detection model was trained. The experimental results showed that the precision rate and F1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${F}_{1}$$\\end{document} score of the proposed method were as high as 98%, while the model’s recall rate reached 99%. Specifically, good detection results were achieved for different types of defects, which provides a reference for the engineering application of internal defect detection. Experimental results verified the effectiveness of the proposed method.

Artificial intelligence-aided detection of rail defects based on ultrasonic imaging data

Railroads are one of society’s fundamental infrastructures, facilitating the transportation of passengers and goods over vast distances. Rail status data is immensely important for ensuring the safe and efficient operation of railroad networks. However, analyzing ultrasonic inspection data is a labor-intensive process and relies heavily on the expertise of experienced inspectors. To detect internal defects of the rail accurately and automatically, this paper proposes a customized image recognition method based on a convolutional neural network with limited B-scan rail image data collected within the industry. The proposed method uses EfficientNet-b7 as the backbone network to fully extract the B-scan rail image data features. With the help of transfer learning and data augmentation techniques, the backbone network is substantially enhanced so that it can understand high-level features of the object without being trained with large-scale B-scan image data. We establish a real-world internal rail defect dataset with 280 B-scan images and test our proposed method. The results reveal that the highest accuracy of the other mainstream CNN-based methods is 76.25% and the accuracy of the traditional method based on a support vector machine classifier trained with Tamura texture and LBP features is 60.00%. Our proposed EfficientNet-b7 model classifies rail defect B-scan images with an accuracy of 85.00%, precision of 84.71%, and recall of 85.00%. Compared to other rail internal defect detection methods, this method is more accurate. With the help of transfer learning and data augmentation, our proposed method achieves better performance and requires less data.

Early detection of thermal instability in railway tracks using piezo-coupled structural signatures

Rail accidents caused by rail track derailments have been a growing concern due to repetitive thermal changes resulting from high temperature stresses in rails due to rail traction and environmental thermal variation. This leads to thermal buckling, which can result in catastrophic failure. Structural health monitoring (SHM) using the electromechanical impedance (EMI) technique has emerged as a promising technology to detect structural deterioration and its severity before it leads to failure. This study used piezoelectric sensors to collect piezo-coupled structural signatures of different rail-joint bars for high-temperature repetitive thermal cycles, which were then analyzed using an impedance analyzer. The results show that the piezo-coupled signatures could identify structural changes, and the damage metric, could be employed for continuous monitoring of structural rail defects due to excessive thermal stress and residual strain. The method also derived piezo-equivalent structural parameters, such as mass, stiffness, and damping, which were very satisfactory in detecting significant changes and consequent damage. Overall, this study presents a pre-emptive experimental method that can see thermal deterioration and instability in rails and rail joints, thereby reducing the risk of rail accidents caused by derailments.

Defect detection of ferromagnetic rail using EMAE-based peak-to-peak method and confidence probability indicator

Rail defect detection holds a vital role in bolstering the safety, improving operational efficiency, and optimizing the lifespan of infrastructures in railway transportation systems. This paper proposes an electromagnetic acoustic emission-based peak-to-peak (EMAE-PTP) method along with a dedicated confidence probability indicator (CPI) for ferromagnetic rail defect detection. Firstly, a comprehensive simulation model that blends Lorentz forces with magnetostrictive effects is built up, affirming the theoretical practicability of the proposed EMAE-PTP method in ferromagnetic rail defect detection. Taking into consideration of the contingency and difference in actual signals acquisition, a special indicator, namely CPI, is formulated as the defect evaluation threshold. Based on the Chebyshev inquality and the time-domain characteristic of acquired signals, this CPI delineates the range of peak-to-peak amplitudes related to non-defective state, with a confidence level up to 96%. By doing so, the numerically segregation of defect signals can be accomplished with ease. In addition, according to the detection coefficient calculated from CPI, the suitable excitation conditions for electromagnetic acoustic emission application are determined. In conclusion, the efficacy of the proposed approach for ferromagnetic rails defect detection is substantiated, encompassing a holistic assessment of both its theoretical underpinnings and experimental manifestations.

Specificity autocorrelation integration network for surface defect detection of no-service rail

Rails are critical to the safe transportation of railway system, and their surface quality is a vital aspect to consider. Existing defect detection methods struggle to identify irregular defect boundaries and distinguish the similarity of foreground and background. To address these issues, depth images are introduced to detect rail defects. However, the existing RGB-D SOD methods usually fuse two modalities without considering modality-specific characteristics. In this paper, we propose a specificity autocorrelation integration network (SAINet) for surface defect detection of rails. SAINet enhances defect detection performance by exploring autocorrelation features of a single modality and the specificity of each modality. Two decoders are carefully designed to capture the specific characteristics of each modality. Moreover, we propose a cross-modal autocorrelation attention fusion (CAAF) to effectively utilize the two modalities of information. It obtains autocorrelation features of RGB images through dilated convolution and attention modules, introducing depth features to locate defects more accurately. We design a multi-modal feature integration block (MFIB) to supplement the cross-modal features with modality-specific features output by each individual decoder, in order to boost SOD performance. SAINet's performance is verified on the NEU RSDDS-AUG dataset. Our network achieves the best results compared to the 25 state-of-the-art methods. We also validate SAINet's generalization performance on six other benchmark datasets, where the experiments show it competes well on these datasets. The code and results are available at https://github.com/VDT-2048/SAINet.

FS-RSDD: Few-Shot Rail Surface Defect Detection with Prototype Learning.

As an important component of the railway system, the surface damage that occurs on the rails due to daily operations can pose significant safety hazards. This paper proposes a simple yet effective rail surface defect detection model, FS-RSDD, for rail surface condition monitoring, which also aims to address the issue of insufficient defect samples faced by previous detection models. The model utilizes a pre-trained model to extract deep features of both normal rail samples and defect samples. Subsequently, an unsupervised learning method is employed to learn feature distributions and obtain a feature prototype memory bank. Using prototype learning techniques, FS-RSDD estimates the probability of a test sample belonging to a defect at each pixel based on the prototype memory bank. This approach overcomes the limitations of deep learning algorithms based on supervised learning techniques, which often suffer from insufficient training samples and low credibility in validation. FS-RSDD achieves high accuracy in defect detection and localization with only a small number of defect samples used for training. Surpassing benchmarked few-shot industrial defect detection algorithms, FS-RSDD achieves an ROC of 95.2% and 99.1% on RSDDS Type-I and Type-II rail defect data, respectively, and is on par with state-of-the-art unsupervised anomaly detection algorithms.

A Guided Wave Approach to Defect Detection in Switch Rail

An approach to detect switch rail defect based on ultrasonic guided wave technology is studied. Eight typical cross‐sections are chosen from the switch rail, and each cross‐section's theoretical dispersion curves and wave structures are derived using the semianalytical finite‐element method. The dispersion curve of eight sections has 262 modes at 60 kHz. Based on k‐means clustering analysis algorithm, 25 kinds of classification results are obtained from 262 modes. According to the mean and variance of energy, an optimal set of modes with uniform amplitude over the entire section of the switch rail is selected. The optimal excitation point is determined based on the vibration energy. The defect echo signal is obtained by excitation of these guided wave modes. Field experiment results show that by comparing the sum of the differences between the collected and basic data and setting a certain threshold, the internal defect detection of the switch guide rail can be realized. The research results herein are valuable for analyzing the dispersion characteristics and realizing the nondestructive testing of the variable cross‐section waveguide.

Comparative analysis of damages and defects in rails of different railway transport systems

This article provides an analysis of the accumulation of detected damages and defects in the rails of two transportation systems - urban (metro) and mainline railway transport. Summarizing the analysis conducted, it should be noted that defects according to figures 10 and 11 are more often encountered on the tracks of the Kyiv Metro. At the same time, defects according to figure 27 are more often encountered on the tracks of the mainline railway transport, and it is also worth noting a wider range of defects, the share of which does not exceed 5%. According to the authors, this is related to the peculiarities of the conditions for operating transportation systems, particularly the load on the axle of the moving equipment, the movement characteristics, and other operational parameters. In order to make more accurate conclusions, it is necessary to perform a number of works using mathematical modeling methods to take into account the influence of individual parameters on the accumulation of defects and damages in rails.

The impact of short-wave effects on deterioration of track geometry

Short wavelength rail defects or short-wave effects of rail have a significant influence on the system behaviour of railways. The influence of additional dynamic loads due to short-wave effects on component wear and the general quality behaviour of track is shown by several research papers. However, the described effect cannot yet be quantified comprehensively. This paper attempts to fill this gap. With the help of extensive data analysis, we can show that short-wave effects lead to a track geometry that is up to 50% worse than on average. Furthermore, we show examples illustrating the influence of untreated welded joints on the development of isolated defects of the track geometry. A detailed in-depth analysis of sections with different boundary conditions finally shows that short-wave effects have a significant influence on track geometry if the amplitude of the effect exceeds 0.15 mm. This value can be recommended as a threshold value in Austria and as a reference value in other countries.

Influence of Varying Operational Parameters on the Defect Detection Performance of a High-Speed Ultrasonic Rail Inspection System During Field Tests

BackgroundContinuous monitoring is essential for detecting internal defects in rails and prevent derailment related accidents. Existing techniques do not facilitate continuous monitoring because they require specialized test cars and can only operate at speeds of up to 30 mph.ObjectiveThe objective of this study is to evaluate the performance of a high-speed rail inspection system using a non-contact ultrasonic technique with the potential of operating at train revenue speeds.MethodsThe technique utilizes air-coupled transducers that record the ultrasonic guided waves generated by the rail-wheel contact and does not require a controlled acoustic source of excitation. A modified version of the traditional Welch’s periodogram technique is utilized to extract the Green’s function between two points on the rail. The passively extracted Green’s function is then analysed statistically to detect structural discontinuities (e.g., defects) in the rail.ResultsResults from fields tests performed at the Transportation Technology Centre (TTC) in Pueblo, CO, USA, demonstrate possible test speeds as high as 80 mph. From these field tests, the performance of the system is evaluated using Receiver Operating Characteristic (ROC) curves for a range of different operational parameters including test speed, location of the sensors relative to the locomotive (source), signal-to-noise ratio (SNR) of the raw signals, SNR of the reconstructed transfer function, baseline distribution length in the statistical analysis, wheel-rail interactions, and redundancies introduced from multiple runs over the same track.ConclusionsThis study presents the current stage of development and performance of the passive rail inspection system with full-scale experiments under field conditions. The results indicate the potential of the system to operate at high speeds as well as possible avenues of future improvement to the system.