• Journal of the Korean Society for Nondestructive Testing
• /
• v.32 no.6
• /
• pp.696-702
• /
• 2012

In this study, a noncontact laser ultrasonic system is proposed for rail defect detection. An Nd-Yag pulse laser is used for generation of ultrasonic waves, and the corresponding ultrasonic responses are measured by a laser Doppler vibrometer. For the detection of rail surface damages, the shape of the excitation laser beam is transformed into a line. On the other hand, a point source laser beam is used for the inspection of defects inside a rail head. Then, the interactions of propagating ultrasonic waves with defects are examined using actual rail specimens. Amplitude attenuation was mainly observed for a surface crack, and reflections were most noticeable from an internal damage. Finally, opportunities and challenges associated with real-time rail inspection from a high-speed train are discussed.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.66 no.5
• /
• pp.870-876
• /
• 2017

In this paper, we proposed the automatic vision inspection system using multi-layer perceptron to detect the defects occurred on rail surface. The proposed system consists of image acquisition part and analysis part. Rail surface image is acquired as equal interval using line scan camera and lighting. Mean filter and dynamic threshold is used to reduce noise and segment defect area. Various features to characterize the defects are extracted. And they are used to train and distinguish defects by MLP-classifier. The system is installed on HEMU-430X and applied to analyze the rail surface images acquired from Honam-line at high speed up to 300 km/h. Recognition rate is calculated through comparison with manual inspection results.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.2
• /
• pp.505-510
• /
• 2024

Since rolling contact fatigue cracks can always occur on the rail surface, which is the contact surface between wheels and rails, railway rails require thorough inspection and diagnosis to thoroughly inspect the condition of the cracks and prevent breakage. Recent detailed guidelines on the performance evaluation of track facilities present the requirements for methods and procedures for track performance evaluation. However, diagnosing and grading rail surface damage mainly relies on external inspection (visual inspection), which inevitably relies on qualitative evaluation based on the subjective judgment of the inspector. Therefore, in this study, we conducted a deep learning model study for rail surface defect detection using Fast R-CNN. After building a dataset of rail surface defect images, the model was tested. The performance evaluation results of the deep learning model showed that mAP was 94.9%. Because Fast R-CNN has a high crack detection effect, it is believed that using this model can efficiently identify rail surface defects.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.2
• /
• pp.511-516
• /
• 2024

The recently enacted detailed guidelines on the performance evaluation of track facilities presented the necessary requirements regarding the evaluation procedures and implementation methods of track performance evaluation. However, the grade of rail surface damage is determined by external inspection (visual inspection), and there is no choice but to rely only on qualitative evaluation based on the subjective judgment of the inspector. Therefore, in this study, we attempted to develop a diagnostic application that can diagnose rail internal defects using rail surface damage. In the field investigation, rail surface damage was investigated and patterns were analyzed. Additionally, in the indoor test, SEM testing was used to construct image data of rail internal damage, and crack length, depth, and angle were quantified. In this study, a deep learning model (Fast R-CNN) using image data constructed from field surveys and indoor tests was applied to the application. A rail surface damage diagnosis application (App) using a deep learning model that can be used on smart devices was developed. We developed a smart diagnosis system for rail surface damage that can be used in future track diagnosis and performance evaluation work.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.1
• /
• pp.585-590
• /
• 2024

In this study, rail surface defects are increasing due to the aging of urban railway rails, but in the detailed guidelines for track performance evaluation established by the country, rail surface damage is inspected with the naked eye of engineers and simple measuring tools. With the recent enactment of the Track Diagnosis Act, a large budget has been invested and the volume of rail diagnosis is rapidly increasing, but it is difficult to secure the reliability of diagnosis results using labor-intensive visual inspection techniques. It is very important to discover defects in the rail surface through periodic track tours and visual inspection. However, evaluating the severity of defects on the rail surface based on the subjective judgment of the inspector has significant limitations in predicting damage inside the rail. In this study, the rail internal crack characteristics due to rail surface damage were studied. In field measurements, rail surface damage locations were selected, samples of various damage types were collected, and the rail surface damage status was evaluated. In indoor testing, we intend to analyze the correlation between rail surface defects and internal defects using a electron scanning microscope (SEM). To determine the crack growth rate of urban railway rails currently in use, the Gaussian probability density function was applied and analyzed.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.3
• /
• pp.845-849
• /
• 2024

Currently, rail surface defects are increasing due to the aging of urban railway rails, but in the detailed guidelines for track performance evaluation established by the country, rail surface damage is inspected with the naked eye of an engineer and with simple measuring tools. It is very important to discover defects in the rail surface through periodic track tours and visual inspection. However, evaluating the severity of defects on the rail surface based on the subjective judgment of the inspector has significant limitations in predicting damage inside the rail. In this study, the characteristics of cracks inside the rail due to rail surface damage were studied. In field measurements, rail surface damage was selected, old rail samples were collected in the acceleration and braking sections, and a scanning electron microscope (SEM) was used to evaluate the rail surface damage was used to analyze the crack characteristics. As a result of the analysis, the crack mechanism caused by the running train and the crack characteristics of the acceleration section where cracks occur at an angle rising toward the rail surface were experimentally proven.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.25 no.5
• /
• pp.380-385
• /
• 2016

Railway tracks are repeatedly overstressed and damaged owing to increase in passing tonnage and numerous contact cycles between wheels of train and rails. In order to ensure safe train operation, heat-treated rails are used in addition to regular inspection and maintenance of these rails. Normal rails were treated using ultrasonic nanocrystal surface modification (UNSM) to strengthen the surface of rails. A few changes in surface properties were detected with respect to hardness and compressive residual stress after UNSM treatment. Wear and rolling contact fatigue tests were performed using rails whose surfaces were hardened by UNSM and heat-treated rails. The amount of wear and fatigue life cycles were measured to estimate the effect of UNSM on the rail material. The material of the surfacehardened rail showed improved wear and rolling contact fatigue properties.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.26 no.6
• /
• pp.411-416
• /
• 2006

The critical subject of transverse crack detection in a rail head is treated in this paper. Conventional bulk wave ultrasonic techniques oftenfail because of shelling and other surface imperfections that shield the defects that lie below the shelling. A guided wave inspection technique is introduced here that can send ultrasonic energy along the rail under the shelling with a capability of finding the deleterious transverse crack defects. Dispersion curves are generated via a semi analytical finite element technique along with a hybrid guided wave finite element technique to explore the most suitable modes and frequencies for finding these defects. Sensor design and experimental feasibility experiments are also reported.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.12
• /
• pp.6043-6062
• /
• 2019

Low-rank matrix decomposition has shown its capability in many applications such as image in-painting, de-noising, background reconstruction and defect detection etc. In this paper, we consider the texture background of rail track images and the sparse foreground of the defects to construct a low-rank matrix decomposition model with block sparsity for defect inspection of rail tracks, which jointly minimizes the nuclear norm and the 2-1 norm. Similar to ADM, an alternative method is proposed in this study to solve the optimization problem. After image decomposition, the defect areas in the resulting low-rank image will form dark stripes that horizontally cross the entire image, indicating the preciselocations of the defects. Finally, a two-stage defect extraction method is proposed to locate the defect areas. The experimental results of the two datasets show that our algorithm achieved better performance compared with other methods.

• Proceedings of the KSR Conference
• /
• 2007.05a
• /
• pp.115-118
• /
• 2007

The geometry of wheel and rail profiles is the primary contributor to wheel and rail interaction. These profiles interact to influence truck steering, vehicle lateral stability, wheel/rail wear and surface damage. Maintaining good control of the profiles is one of the keys to ensuring preferred wheel and rail interaction. Transportation Technology Center, Inc., Pueblo, Colorado, is developing an automated measurement system for wheel/rail contact condition inspections supported by AAR(Association of American Railroads). The system uses a modified version of $WRTOL^{TM}$ (Wheel/Rail Tolerances)--software that performs extensive analysis of wheel and rail contact conditions