Vision-Based Position Deviation Measurement of Rail Corrugation Chord Measuring Points Under Bi-Linear Laser Assistance

Chen, Liang; Li, Yan‐Fu; Ma, Ziji; Liu, Hongli; Mao, Weijie

doi:10.1109/access.2021.3062663

Cited by 6 publications

(4 citation statements)

References 22 publications

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“…Figure 10 illustrates the process diagram of the rail-surface localization method. Aiming to solve the problem that the rail surface is not sensitive to lighting and reflection characteristics, Chen et al [61] proposed a bi-linear laser-assisted method. This method can locate the railhead boundary and measure the position deviations of the rail corrugation measuring point based on the string, and no additional light source is required.…”

Section: Computer-vision-based Methods For Corrugation Automatic Dete...mentioning

confidence: 99%

Concise Historic Overview of Rail Corrugation Studies: From Formation Mechanisms to Detection Methods

Wang,

Huang,

Wang

et al. 2024

Buildings

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Rail corrugation is a serious problem in a railway transportation system, aggravating the operational risk and shortening the lifetime of train–track system. In order to ensure the safety and reliability of the railway system, the detection of rail corrugation is very important. Thus, this study systematically summarizes the recent research progress of rail corrugation. First, this study introduces the definition of rail corrugation and the classification criteria. Then, the formation mechanism of rail corrugation is analyzed in detail, and its adverse consequences are investigated. Further, this study summarizes several main detection methods, which are corrugation-detection methods based on acceleration measurements, wavelet transform methods for corrugation evaluation, computer-vision-based methods for corrugation automatic detection, digital filtering algorithms for rail corrugation detection, and others. In this study, the formation mechanism and detection methods of rail corrugation are systematically described, and various corrugation-detection methods are also introduced in detail. This study not only provides a scientific basis for railway maintenance, but also lays a solid foundation for future experimental design and data analysis. This study can also guide engineering practice to improve the reliability and safety of railway systems. It also provides useful experience for future railway-engineering design and planning, as well as safer and more reliable operation. In general, this study can provide technical support for the detection of rail corrugation to ensure the safety of the rail–track system.

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Section: Computer-vision-based Methods For Corrugation Automatic Dete...mentioning

confidence: 99%

Concise Historic Overview of Rail Corrugation Studies: From Formation Mechanisms to Detection Methods

Wang,

Huang,

Wang

et al. 2024

Buildings

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“…The longitudinal rail profile must be measured following EN 13231-2 [ 12 ] in the area between the highest point of the rail surface and up to 15

towards the inner edge of the rail. Chen et al [ 13 ] have developed a positioning system for a measuring device installed on the vehicle to measure the longitudinal rail profile. The system aims to measure at the center of the rail head.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of the Running Surface of Railway Tracks Based on Laser Profilometer Data and Supervised Machine Learning

Mauz,

Wigger,

Gota

et al. 2024

Sensors

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The measurement of the longitudinal rail profile is relevant to the condition monitoring of the rail infrastructure. The running surface is recognizable as a shiny metallic area on top of the rail head. The detection of the running surface is crucial for vehicle-based rail profile measurements, as well as for defect detection. This paper presents a methodology for the automatic detection of the running surface based on a laser profilometer. The detection of the running surface is performed based on the light reflected from the rail surface. Three rail surfaces with different surface conditions are considered. Supervised machine learning is applied to classify individual surface elements as part of the running surface. Detection by a linear support vector machine is performed with accuracy of >90%. The lateral position of the running surface and its width are calculated. The average deviation from the labeled widths varies between −1.2mm and 5.6mm. The proposed measurement approach could be installed on a train for the future onboard monitoring of the rail network.

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“…The system could be operated up to a speed of

with a maximum positioning error of

. Chen et al [ 21 ] further developed the concept and used two laser lines to transverse the driving direction for the optical support of the camera due to illumination conditions. The positioning error in this application was

.…”

Section: Introductionmentioning

confidence: 99%

“…The positioning error in this application was

. Chen et al [ 21 ] established a maximum driving speed of

.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Roughness Measurement of Railway Tracks: Running Surface Detection and Compensation of Lateral Movements for Optical Measurements on a Train

Mauz,

Wigger,

Griesbaum

et al. 2023

Sensors

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Rolling noise is a significant contributor to railway noise. Wheel and rail roughness are decisive for the emitted noise level. An optical measurement method installed on a moving train is suitable for closer monitoring of the rail surface condition. A measurement setup based on the chord method requires the sensors to be positioned in a straight line along the direction of measurement and in a stable lateral position. Measurements should always be performed within the shiny and uncorroded running surface, even when there are lateral movements of the train. In this study, concepts for the detection of the running surface and the compensation of lateral movements are investigated in a laboratory setting. The setup consists of a vertical lathe with a ring-shaped workpiece that incorporates an implemented artificial running surface. The detection of the running surface based on laser triangulation sensors and a laser profilometer is investigated. It is shown that the running surface can be detected using a laser profilometer that measures the intensity of the reflected laser light. It is possible to detect the lateral position and the width of the running surface. A linear positioning system is proposed to adjust the lateral position of the sensors based on the running surface detection of the laser profilometer. When the lateral position of the measuring sensor is disturbed by a movement with a wavelength of 18.85 m, the linear positioning system can keep the laser triangulation sensor inside the running surface for 98.44% of the measured data points at a velocity of approximately 7.5 km h−1. The mean positioning error is 1.40 mm. By implementing the proposed system on the train, future studies can be conducted to examine the lateral position of the running surface as a function of the various operational parameters of the train.

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Vision-Based Position Deviation Measurement of Rail Corrugation Chord Measuring Points Under Bi-Linear Laser Assistance

Cited by 6 publications

References 22 publications

Concise Historic Overview of Rail Corrugation Studies: From Formation Mechanisms to Detection Methods

Concise Historic Overview of Rail Corrugation Studies: From Formation Mechanisms to Detection Methods

Automatic Detection of the Running Surface of Railway Tracks Based on Laser Profilometer Data and Supervised Machine Learning

Acoustic Roughness Measurement of Railway Tracks: Running Surface Detection and Compensation of Lateral Movements for Optical Measurements on a Train

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