A new methodology for optimal rail track condition measurement using acceleration signals

Chellaswamy, C.; Muthammal, R; Geetha, T.

doi:10.1088/1361-6501/aabe48

Cited by 16 publications

(6 citation statements)

References 39 publications

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“…Step 3: The MF is done by the sum of the difference between the two vectors with the third vector, creating the trial vector (TU x ). It can be expressed based on ( 36 ) as:…”

Section: Adaptive Differential Evolution Based Noise Measurementmentioning

confidence: 99%

Adaptive Differential Evolution Optimization-Based Noise-Level Measurement for High-Speed Railways

Babu

Chellaswamy²,

Geetha³

2021

Transportation Research Record: Journal of the Transportation R

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This paper deals with the possibilities of estimating noise pollution created by high-speed railway systems in nearby locations. Railway systems have significant effects on the environment. Therefore, a college campus situated near a high-speed railway was selected as the study area. In this paper, an adaptive differential evolution optimization (ADEO) algorithm-based noise-level measurement is proposed. Various measurements such as the noise levels indoors, outdoors, and near the track were carried out in the college area and applied to ADEO for optimization. A study of the impact of railway noise on student learning was made. ADEO was used to predict the maximum noise level and the maximum noise distribution in the college area through the model. An experimental study was performed, and the results were compared with the estimated results. The results indicated the consistency of both the estimated and experimental results and the error as less than 1 dBA; the noise level exceeded 65 dBA in a few classrooms. Therefore, the proposed noise measurement for high-speed railway based on the ADEO technique has been considered as the most effective and superior optimization tool.

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“…Step 3: The MF is done by the sum of the difference between the two vectors with the third vector, creating the trial vector (TU x ). It can be expressed based on ( 36 ) as:…”

Section: Adaptive Differential Evolution Based Noise Measurementmentioning

confidence: 99%

Adaptive Differential Evolution Optimization-Based Noise-Level Measurement for High-Speed Railways

Babu

Chellaswamy²,

Geetha³

2021

Transportation Research Record: Journal of the Transportation R

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“…(2) Track irregularities are the main causes of train cabin vibration [28][29][30]. Track irregularities and the dynamic response of rolling stock are closely related [31].…”

Section: Causes Of Train Cabin Vibrationmentioning

confidence: 99%

Data-Driven Bias Correction and Defect Diagnosis Model for In-Service Vehicle Acceleration Measurements

Bai

Liu

2020

Sensors

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Track quality instruments use low-cost accelerometers placed on or attached to the floors of operating trains, and these instruments collect substantial amounts of data over short inspection periods. The measurements collected by the instruments are the main data source for track irregularity evaluation. However, considerable measurement bias exists in the vertical and lateral vibration data obtained from such instruments. False positive track vibration defects detected by track quality instruments occur frequently. This results in considerable time and effort being expended needlessly because maintenance workers have to visit the railway track sites to check and review the track vibration defects. Therefore, we propose a model for data-driven bias correction and defect diagnosis for in-service vehicle acceleration measurements based on track degradation characteristics. Substantial amounts of historical track measurement data from different inspection methods were mined extensively to eliminate the false positive detection of track vibration defects and diagnose the causes of track vibration defects. Actual measurement data from the Lanxin Railway were used to validate our proposed model. The success rate achieved in identifying false positive track vibration defects was 84.1%, and that in track vibration defect diagnosis was 75.8%. These high success rates suggest that the proposed model can be of practical use in improving railway track maintenance management.

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“…The algorithm is closely related to principal component analysis and extracts the principal dynamics from measurement data. A differential evolution technique based track measurement is presented in the paper [35], where the axle box measurements are considered. The new method enables to estimate the exact location of the irregularity.…”

Section: Vol 15 Issue 2/2019 101-114mentioning

confidence: 99%

Multifractal Analysis of the Common Crossing Track-Side Measurements

Sysyn

Ižvolt

Nabochenko

et al. 2019

Civil and Environmental Engineering

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The deterioration common crossing elements increase the need for the maintenance of crossings and significantly reduce the reliability, availability and safety of railway traffic. The present paper introduces the results of experimental investigation of common crossing monitoring while its deterioration during the lifecycle of crossing. The conventional methods of common crossing condition estimation with time-based features like maximal accelerations, etc. are low appropriate as statistically significant condition indicators. The proposed multifractal analysis enables to extract the significant features from acceleration measurements. The extracted features are fused together with information about the train velocities and longitudinal position of wheels impact with the Lasso regularisation and multivariate linear regression. A ranking of feature importance is done and the redundant features are identified. The developed condition indicator has good correlation to the crossing lifetime and is simple for interpretation.

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A new methodology for optimal rail track condition measurement using acceleration signals

Cited by 16 publications

References 39 publications

Adaptive Differential Evolution Optimization-Based Noise-Level Measurement for High-Speed Railways

Adaptive Differential Evolution Optimization-Based Noise-Level Measurement for High-Speed Railways

Data-Driven Bias Correction and Defect Diagnosis Model for In-Service Vehicle Acceleration Measurements

Multifractal Analysis of the Common Crossing Track-Side Measurements

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