Effect of Design variables of Rail Surface Measuring Device on Acoustic Roughness and Spectral Analysis

Jeong, Wootae; Jeon, Seungwoo; Jeong, Dahae; Choi, Han Shin

doi:10.7782/jksr.2017.20.4.440

Cited by 4 publications

(4 citation statements)

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“…There have been also proposed alternative solutions such as the one of chord offset synchronizing, which assumes that the rail surface is a sinusoidal wave consisting of various wavelengths and uses multiple sensors so that each one compensates for the measured values of the others. This procedure has been used to prepare for the structural shortcomings of mobile measuring systems equipped with displacement sensors [ 13 , 14 ]. Visual sensing, such as two-dimensional image recognition, is also employed in the measurement and/or detection of the abnormal fastener in the rail-track inspection system.…”

Section: Related Workmentioning

confidence: 99%

Robotic Railway Multi-Sensing and Profiling Unit Based on Artificial Intelligence and Data Fusion

Minea

Dumitrescu

Dima

2021

Sensors

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This article presents the research and results of field tests and simulations regarding an autonomous/robotic railway vehicle, designed to collect multiple information on safety and functional parameters of a surface railway and/or subway section, based on data fusion and machine learning. The maintenance of complex railways, or subway networks with long operating times is a difficult process and intensive resources consuming. The proposed solution delivers human operators in the fault management service and operations from the time-consuming task of railway inspection and measurements, by integrating several sensors and collecting most relevant information on railway, associated automation equipment and infrastructure on a single intelligent platform. The robotic cart integrates autonomy, remote sensing, artificial intelligence, and ability to detect even infrastructural anomalies. Moreover, via a future process of complex statistical filtering of data, it is foreseen that the solution might be configured to offer second-order information about infrastructure changes, such as land sliding, water flooding, or similar modifications. Results of simulations and field tests show the ability of the platform to integrate several fault management operations in a single process, useful in increasing railway capacity and resilience.

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Section: Related Workmentioning

confidence: 99%

Robotic Railway Multi-Sensing and Profiling Unit Based on Artificial Intelligence and Data Fusion

Minea

Dumitrescu

Dima

2021

Sensors

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“…However, the mobile measurement platform does not have a fixed reference for sensor measurement and the sensor itself may be tilted, causing potential for distortion in the size and phase of the measured value. Therefore, new ways have been proposed, such as the chord-based measurement method, which assumes that the longitudinal rail surface is a sinusoidal wave function consisting of various wavelengths and uses more than one sensor to correct the measured value mutually, and have been used to complement the structural limitations of mobile measuring systems with displacement sensors [13,14].…”

Section: Introductionmentioning

confidence: 99%

Spectral Characteristics of Rail Surface by Measuring the Growth of Rail Corrugation

Jeong

2021

Applied Sciences

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Continuous interaction between wheels and rails during train operation results in rail wear and tear. Corrugation of the rail surface is particularly caused by the contact mechanism between train wheel and rail and increases the vibration and dynamic wheel load, and if continued, leads to various defects and breakage of the track. Many devices are used to measure corrugation accurately, but measurement deviation varies greatly by measuring device. The most common corrugation measurement system measures surface roughness with a vibration acceleration sensor or displacement sensor. Corrugation with different pitches can be calculated by assuming the longitudinal rail surface as a chord with variable wavelength. Recent systems use a measurement model applying multiple sensors for more accurate measurement. This study investigated spectral characteristics of rail surface roughness based on long time measurement results. In particular, spectral changes upon the growth of rail surface wear and corrugation were analyzed when using the measurement system applying the chord offset method with multiple sensors. The results of analysis were verified through the field subway section, a running track undergoing corrugation, three months after initial measurement, and compared the measurement results according to the number of sensors. Additionally, the rail surface wavelength bands that affected measurement value according to the number of sensors were analyzed.

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“…However, such a moving platform results in inaccurate amplitude and phase of the measured values since the reference position for measurement is not fixed and the sensor can be inclined. Therefore, new ways like the chord offset synchronizing method, which assumes that the rail surface is a sinusoidal wave consisting of various wavelengths and uses multiple sensors so that each one compensates for the measured values of the others, have been proposed and used to make up for the structural shortcomings of mobile measuring systems equipped with displacement sensors [16,17].…”

Section: Introductionmentioning

confidence: 99%

Acoustic Roughness Measurement of Railhead Surface Using an Optimal Sensor Batch Algorithm

Jeong

2020

Applied Sciences

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Contact and friction between wheel and rail during train operation is the main cause of the rolling noise for which railways are known. Therefore, it is necessary to accurately measure the surface roughness of wheels and rails to monitor railway noise and predict noise around tracks. Conventional systems developed to measure surface roughness have large deviations in measured values or low repeatability. The recently developed automatic mobile measurement platform known as Auto Rail Checker (ARCer) uses three displacement sensors to reduce measurement deviation and increase the accuracy of existing systems. This paper proposes enhancing the chord offset synchronization algorithm applied to the existing ARCer for high measurement precision with only two displacement sensors. As a result, when the two sensor-based measurement algorithm was applied, the spectrum level at λ = 0.314 m, the wavelength amplification associated with wheel diameter, was reduced to at least 6 dB in comparison with that of the three sensors based algorithm. We also verified the accuracy of the proposed batch algorithm through a field test on an operating rail track with a corrugated rail surface.

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Effect of Design variables of Rail Surface Measuring Device on Acoustic Roughness and Spectral Analysis

Cited by 4 publications

References 0 publications

Robotic Railway Multi-Sensing and Profiling Unit Based on Artificial Intelligence and Data Fusion

Robotic Railway Multi-Sensing and Profiling Unit Based on Artificial Intelligence and Data Fusion

Spectral Characteristics of Rail Surface by Measuring the Growth of Rail Corrugation

Acoustic Roughness Measurement of Railhead Surface Using an Optimal Sensor Batch Algorithm

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