Search Results (60)

A nonlinear ultrasonic time-domain identification method based on chaos sensitivity was proposed in this study. The Duffing chaotic system was introduced into the weak second harmonic identification to realize early detection and quantitative evaluation of fatigue damage in U71Mn steel. First, to ensure the reliability of nonlinear ultrasonic testing, a probe-pressure monitoring device was designed. Through pressure-stability experiments, 16 N was determined as the optimal pressure, which effectively suppresses contact nonlinearity interference and ensures coupling stability. Subsequently, the Duffing chaos detection system was established. The signal-system frequency-matching problem was resolved through time-scale transformation. Simultaneously, the issue of unknown initial phases was resolved using phase traversal compensation. Based on the chaotic system’s sensitivity to specific frequency signals and immunity to noise, the amplitudes of the fundamental wave and second harmonics in the target signals were quantified to calculate the nonlinear coefficient. Experimental results demonstrate that the proposed method can extract these amplitudes directly in the time domain, thereby effectively overcoming the spectral leakage inherent in traditional frequency-domain methods. The nonlinear coefficient of U71Mn steel exhibits a “double-peak” characteristic as fatigue damage increases. Specifically, the first peak appears at approximately 50% of fatigue life, while the second occurs at approximately 80%. This phenomenon is closely correlated with the distinct stages of internal fatigue crack propagation, reflecting a complex damage-evolution mechanism. This study not only provides a novel method for the precise extraction of weak nonlinear signals but also establishes a critical theoretical and experimental foundation for accurate fatigue life prediction for U71Mn rail steel. Full article

To enhance the resilience and sustainability of urban metro systems under operational uncertainties and external disturbances, critical station identification and vulnerability assessment should be further investigated from the perspective of network science. In this paper, the presented comprehensive clustering algorithm and the Pearson correlation coefficient are adopted to explore the origin-destination (OD) passenger flow characteristics on different date classifications, and the different dates should be reasonably classified into three categories, including working day, weekends, and holiday. Meanwhile, this paper proposes the dynamic DomiRank algorithm and flow DomiGCN model to identify critical stations from network structure and function on different data classifications respectively, and further studies the vulnerability property of metro networks under simulated attacks. The Shanghai metro network is selected as case to prove the feasibility and correctness of the model. The results show that the dynamic DomiRank algorithm is relatively effective to identify critical stations from network structure, and the flow DomiGCN model is also relatively effective to identify critical stations from network function. Moreover, simulated attacks to these critical stations detected by the proposed methods can cause more damages than the other methods. These findings provide some supports for protection of metro infrastructure and contribute to the sustainable operation and development of urban rail transit systems. Full article

In rail damage detection, the scale variation of small targets leads to inaccurate extraction of damage morphology and size features, thereby affecting the reliable identification of damage types. The DETR algorithm has been optimized and improved. Firstly, we introduce the convolution–attention fusion module (CAFMAttention) after the two side convolutional layers of the original algorithm; then, we replace the nn.Upsample-based upsampling layer with the Dysample upsampler. Finally, we replace the Conv modules in the two down-sampled convolutional layers with Dual-Conv modules. The results of the comparative experiments show that the recall rate of the improved DETR model in this paper is 0.698, which is 12.2% higher than that of the original DETR model. The accuracy is 0.815, which is 2.3% higher than that of the original DETR model. The average precision (Map@0.5) is 0.741. Compared with the original DETR model, it has been improved by 8.7%. The F1 score is 0.75, which is 8.7% higher than the original DETR model. The frame per second (FPS) transfer rate is 64.94, which is 2.6% higher than that of the original DETR model. The proposed DETR algorithm can detect rail damage under complex working conditions well, with high accuracy and robustness, and better meet the requirements of practical actual rail detection. Full article

To eliminate the risk of damage to buried pipelines during excavation, a survey in advance or on the spot is necessary. Here we propose a wireless rotable magnetic sensor array to detect underground ferromagnetic pipelines. It consists of several sensing nodes placed on a rail, which can rotate automatically or manually. We adopted rotating rather than translating the array since translation is difficult on uneven or muddy ground. Moreover, we could judge the existence and orientation of a pipeline by simply checking the periodic variation of measured data without resorting to complex inversion algorithms. Field experiments showed that the equipment could provide a decimeter-level locating accuracy for both the horizontal offset and buried depth, and a strike angle error of a few degrees, which meet general engineering application requirements. Full article

Squat is a type of rail defect that frequently poses challenges for railway tracks, as they generate dynamics and accelerate track degradation. Detecting rail squats is resource-intensive, given their relatively small size compared to the railway track. Often, by the time they are detected, damage has usually already occurred in other track components. Currently, rail squats are primarily detected using dedicated railway measurement vehicles. There has been a recent trend in research towards utilizing trains in regular traffic to monitor the condition of railway tracks. However, there is a lack of research and general guidelines regarding the optimal placement of accelerometers or sensors on trains for squat detection. In this study, multibody simulation software GENSYS Rel.2209 is employed to simulate a passenger train traversing rail squats under various scenarios, with each scenario characterized by a distinct set of typical feature values for the squats. The results demonstrate that the front wheel set, positioned closest to the defects, exhibits the highest sensitivity to vertical accelerations. Squat length is much more sensitive than depth for detection at typical speeds, and accelerometers on bogies or the car body require speeds below 40 km/h to ensure reliability. The acceleration response mechanism during squat traversal is explored, revealing the effects of varying squat geometries and train speeds. This finding enables a detection method capable of locating squats and estimating their length with over 90% accuracy. Practical recommendations are provided for optimizing squat detection systems, including squat width detection, sensor selection criteria, and suggested train speeds. It offers a pathway to detect squat more efficiently with optimized installation locations of accelerometers on a train. Full article

A wheel flat is the most common fault of a railway freight car, a type of complex transport equipment. A wheel flat will cause continuous regular impact on the rail, damage the rail and the railway structure, affecting the safety and stability of rail transport. This article studied the relationship between wheel flats and wheel–rail impacts using multi-body dynamics simulation through SIMPACK and, through a field test, validates the detection of a flat wheel. The results show that using the simulation method can obtain similar data to the measured wheel–rail force in the wayside detection device. The simulation data show that the data collected by 14 shear vertical force acquisition channels can completely cover the wheel surface of the heavy-duty railway 840 mm diameter wheel. According to the flat length-speed-impact diagram, the mapping relationship can be fitted using polynomial regression. Based on the measured wheel–rail impact forces, the size of wheel flats can then be deduced from this established mapping relationship. Through a field test, the detection method has been validated. Full article

From the point of view of the intelligent operation and maintenance of high-speed train tracks, this paper examines the research status of high-speed train rail damage detection technology in the field of high-speed train track operation and maintenance detection in recent years, summarizes the damage detection methods for high-speed trains, and compares and analyzes different detection technologies and application research results. The analysis results show that the detection methods for high-speed train rail damage mainly focus on the research and application of non-destructive testing technology and methods, as well as testing platform equipment. Detection platforms and equipment include a new type of vortex meter, integrated track recording vehicles, laser rangefinders, thermal sensors, laser vision systems, LiDAR, new ultrasonic detectors, rail detection vehicles, rail detection robots, laser on-board rail detection systems, track recorders, self-moving trolleys, etc. The main research and application methods include electromagnetic detection, optical detection, ultrasonic guided wave detection, acoustic emission detection, ray detection, vortex detection, and vibration detection. In recent years, the most widely studied and applied methods have been rail detection based on LiDAR detection, ultrasonic detection, eddy current detection, and optical detection. The most important optical detection method is machine vision detection. Ultrasonic detection can detect internal damage of the rail. LiDAR detection can detect dirt around the rail and the surface, but the cost of this kind of equipment is very high. And the application cost is also very high. In the future, for high-speed railway rail damage detection, the damage standards must be followed first. In terms of rail geometric parameters, the domestic standard (TB 10754-2018) requires a gauge deviation of ±1 mm, a track direction deviation of 0.3 mm/10 m, and a height deviation of 0.5 mm/10 m, and some indicators are stricter than European standard EN-13848. In terms of damage detection, domestic flaw detection vehicles have achieved millimeter-level accuracy in crack detection in rail heads, rail waists, and other parts, with a damage detection rate of over 85%. The accuracy of identifying track components by the drone detection system is 93.6%, and the identification rate of potential safety hazards is 81.8%. There is a certain gap with international standards, and standards such as EN 13848 have stricter requirements for testing cycles and data storage, especially in quantifying damage detection requirements, real-time damage data, and safety, which will be the key research and development contents and directions in the future. Full article

Distributed acoustic sensing (DAS) is a highly effective method of monitoring all kinds of intrusions on railway tracks. These intrusions represent a real problem in the railway sector, as they can lead to human deaths or damage to railway tracks, and these intrusions may be human or animal. A fiber was deployed along 12 km of track in a railway test center, enabling us to acquire data day and night. A data acquisition campaign was carried out in April 2023 to capture the signatures of several scenarios (walking, digging, falling rocks, etc.) in order to train machine learning models and prevent any intrusion by detecting and classify these intrusion. The study shows the diversity of signals that fiber can acquire in the rail sector and the machine learning model performance. Signals associated with the presence of animals are also presented. Full article

With the rapid expansion of railway networks and increasing operational complexity, intelligent rail damage detection has become crucial for ensuring safety and improving maintenance efficiency. Traditional physical inspection methods (e.g., ultrasonic testing, magnetic flux leakage) are limited in terms of efficiency and environmental adaptability. This study proposes a machine vision-based approach leveraging deep learning to identify four primary types of rail damages: corrugations, spalls, cracks, and scratches. A self-developed acquisition device collected 298 field images from the Chongqing Metro system, which were expanded into 1556 samples through data augmentation techniques (including rotation, translation, shearing, and mirroring). This study systematically evaluated three object detection models—YOLOv8, SSD, and Faster R-CNN—in terms of detection accuracy (mAP), missed detection rate (mAR), and training efficiency. The results indicate that YOLOv8 outperformed the other models, achieving an mAP of 0.79, an mAR of 0.69, and a shortest training time of 0.28 h. To further enhance performance, this study integrated the Multi-Head Self-Attention (MHSA) module into YOLO, creating MHSA-YOLOv8. The optimized model achieved a significant improvement in mAP by 10% (to 0.89), increased mAR by 20%, and reduced training time by 50% (to 0.14 h). These findings demonstrate the effectiveness of MHSA-YOLO for accurate and efficient rail damage detection in complex environments, offering a robust solution for intelligent railway maintenance. Full article

With the vigorous development of speedy railway technology, steel rails, as an important structural infrastructure in speedy railways, play a crucial role in ensuring the safety of the entire speedy railway operation. A brand-new type of speedy rail inspection robot and its fault detection method are proposed to solve a number of problems, such as the difficulty and low accuracy of real-time online detection of rail defects and damage in speedy railways. The brand-new rail inspection robot is driven by two drive wheels and adopts a standard rail gauge of 1435 mm, which ensures its speedy and smooth operation on the track as well as accurate motion posture information. Firstly, 12 common types of surface damage of the rail head were analyzed and classified into five categories based on their damage characteristics. The motion state of the rail inspection robot under the five types of surface damage of the rail head was analyzed and subjected to kinematic analysis. This study analyzed the relationship between the distinctive types of damage and the motion posture of the robot during the inspection of the five types of damage. Finally, experimental tests were conducted, and it was found that the robot’s motion posture would undergo sudden changes when inspecting distinctive types of injuries; the highest error rate was 3%. The effectiveness of this method was verified through experiments, and the proposed new track detection robot can greatly improve the track detection efficiency of high-speed railways and has specific academic research value and practical application value. Full article

Cement–asphalt (CA) mortar voids in earth’s structure are prone to inducing abnormal vibrations in vehicle and track systems and are more difficult to recognize. In this paper, a vehicle–ballastless track coupling model considering cement–asphalt mortar voids is established and the accuracy of the model is verified. There are two main novel results: (1) The displacement of the track slab in the ballastless track structure is more sensitive to the void length. Voids can lead to blocked vibration transmission between the ballastless track slab and concrete base. (2) The wheel–rail vibration acceleration is particularly sensitive to voids in cement–asphalt mortar, making the bogie pendant acceleration a key indicator for detecting such voids through amplitude changes. Additionally, the train body pendant acceleration provides valuable feedback on the cyclic characteristics associated with single-point damage in the cement–asphalt mortar, thereby enhancing the accuracy of dynamic inspections for vehicles. In the sensitivity ordering of the identification indexes of voids, the bogie’s vertical acceleration in high-speed trains > the nodding acceleration of the bogie > the vehicle’s vertical acceleration. Adaptive suspension parameters can be designed to accommodate changes in track stiffness. Full article

Common rail systems are a key component of modern diesel engines and highly increase their performance. During their working lifetime, there could be critical damages or failures related to aging, like backlash or friction, or out-of-spec operating conditions, like low-quality fuel with, e.g., the presence of water or particles or a high percentage of biodiesel. In this work, suitable data-driven methods are adopted to develop an automatic procedure to monitor, diagnose, and estimate some types of faults in common rail systems. In particular, the pressure control loop operating within the engine control unit is investigated; the system is described using a Hammerstein model composed of a nonlinear model for the control valve behavior and an extended linear model for the process dynamics, which also accounts for the presence of external disturbances. Three different sources of oscillations can be successfully detected and quantified: valve stiction, aggressive controller tuning, and external disturbance. Selected case studies are used to demonstrate the effectiveness of the developed methodology. Full article

Rail surface cracks are widespread damage that can lead to uneven surfaces of railheads and affect traveling safety. Non-destructive testing is needed to inspect rails regularly to ensure the normal operation of railroads. This paper proposes a laser ultrasonic testing method combining variational mode decomposition and diffractive Rayleigh wave time-of-flight to detect tiny cracks on the rail surface quantitatively. The finite element method was combined with experiments to simulate and experimentally investigate cracks of different sizes numerically. In the numerical simulation, the location of the crack was determined by B-scan. Afterward, the interaction between various types of ultrasound and cracks was comparatively analyzed, and the crack size was quantitatively characterized using useful information from the ultrasound signals. The results show that the time-of-flight method can detect arbitrary cracks with low error. Therefore, the experimentally acquired ultrasound signals used the time difference between the diffracted Rayleigh wave and other ultrasound waves to detect the crack information quantitatively. The variational mode decomposition method was used to separate the ultrasonic signals and extract the best surface wave modes to improve the signal-to-noise ratio. The results show that the combination of variational mode decomposition and time-of-flight method can effectively detect the size of cracks. Full article

Railway transportation has been integrated into people’s lives. According to the “Notice on the release of the General Technical Specification of High-speed Railway Power Supply Safety Testing (6C System) System” issued by the National Railway Administration of China in 2012, it is required to install pantograph and slide monitoring devices in high-speed railway stations, station throats and the inlet and exit lines of high-speed railway sections, and it is required to detect the damage of the slider with high precision. It can be seen that the good condition of the pantograph slider is very important for the normal operation of the railway system. As a part of providing power for high-speed rail and subway, the pantograph must be paid attention to in railway transportation to ensure its integrity. The wear of the pantograph is mainly due to the contact power supply between the slide block and the long wire during high-speed operation, which inevitably produces scratches, resulting in depressions on the upper surface of the pantograph slide block. During long-term use, because the depression is too deep, there is a risk of fracture. Therefore, it is necessary to monitor the slider regularly and replace the slider with serious wear. At present, most of the traditional methods use automation technology or simple computer vision technology for detection, which is inefficient. Therefore, this paper introduces computer vision and deep learning technology into pantograph slide wear detection. Specifically, this paper mainly studies the wear detection of the pantograph slider based on deep learning and the main purpose is to improve the detection accuracy and improve the effect of segmentation. From a methodological perspective, this paper employs a linear array camera to enhance the quality of the data sets. Additionally, it integrates an attention mechanism to improve segmentation performance. Furthermore, this study introduces a novel image stitching method to address issues related to incomplete images, thereby providing a comprehensive solution. Full article

In current railway rails, trains are propelled by the rolling contact between iron wheels and iron rails, and the high frequency of train repetition on rails results in a significant load exertion on a very small area where the wheel and rail come into contact. Furthermore, a contact stress beyond the allowable stress of the rail may lead to cracks due to plastic deformation. The railway rail, which is the primary contact surface between the wheel and the rail, is prone to rolling contact fatigue cracks. Therefore, a thorough inspection and diagnosis of the condition of the cracks is necessary to prevent fracture. The Detailed Guideline on the Performance Evaluation of Track Facilities in South Korea specifies the detailed requirements for the methods and procedures for conducting track performance evaluations. However, diagnosing rail surface damage and determining the severity solely rely on visual inspection, which depends on the qualitative evaluation and subjective judgment of the inspector. Against this backdrop, rail surface defect detection was investigated using Fast R-CNN in this study. To test the feasibility of the model, we constructed a dataset of rail surface defect images. Through field investigation, 1300 images of rail surface defects were obtained. Aged rails collected from the field were processed, and 1300 images of internal defects were generated through SEM testing; therefore, a total of 1300 pieces of learning data were constructed. The detection results indicated that the mean average precision was 94.9%. The Fast R-CNN exhibited high efficiency in detecting rail surface defects, and it demonstrated a superior recognition performance compared with other algorithms. Full article