A Novel Description Method for Track Irregularity Evolution

Xu, Peng; Liu, Rengkui; Wang, Feng; Sun, Qiang; Teng, Hualiang

doi:10.1080/18756891.2011.9727886

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…A machine learning model based using a multistage framework is developed by Xu et al. 54 who predicted changes in track irregularity over time. They defined different stages of track irregularity changes based on maintenance thresholds and linear regression was used to predict track degradation in each stage.…”

Section: Track Geometry Degradation Modelsmentioning

confidence: 99%

Track geometry degradation and maintenance modelling: A review

Soleimanmeigouni

Ahmadi

Kumar

2016

Proceedings of the Institution of Mechanical Engineers, Part F:

126

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Increased demand for railway transportation is creating a need for higher train speeds and axle loads. These, in turn, increase the likelihood of track degradation and failures. Modelling the degradation behaviour of track geometry and development of applicable and effective maintenance strategies has become a challenging concern for railway infrastructure managers. During the last three decades, a number of track geometry degradation and maintenance modelling approaches have been developed to predict and improve the railway track geometry condition. In this paper, existing track geometry measures are identified and discussed. Available models for track geometry degradation are reviewed and classified. Tamping recovery models are also reviewed and discussed to identify the issues and challenges of different available methodologies and models. Existing track geometry maintenance models are reviewed and critical observations on each contribution are provided. The most important track maintenance scheduling models are identified and discussed. Finally, the paper provides directions for further research.

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Section: Track Geometry Degradation Modelsmentioning

confidence: 99%

Track geometry degradation and maintenance modelling: A review

Soleimanmeigouni

Ahmadi

Kumar

2016

Proceedings of the Institution of Mechanical Engineers, Part F:

126

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“…Zhu et al, (2013) applied a Gaussian random process to model track irregularities of profile and alignment and studied their power spectral densities. Considering that the evolution of track irregularity is periodic, exponential, and has multiple stages, Xu et al, (2012) employed a multi-stage linear fitting model to describe the track irregularity deterioration process between two adjacent maintenance actions. Lee et al, (2018) combined an artificial neural network (ANN) and support vector regression (SVR) to better represent the deterioration phenomena of track segments for optimizing the maintenance plans in terms of time and cost.…”

Section: Introductionmentioning

confidence: 99%

BIC-Based Data-Driven Rail Track Deterioration Adaptive Piecewise Modeling Framework

Yang

et al. 2021

Front. Mater.

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The records of maintenance activities are required for modeling the track irregularity deterioration process. However, it is hard to guarantee the completeness and accuracy of the maintenance records. To tackle this problem, an adaptive piecewise modeling framework for the rail track deterioration process driven by historical measurement data from the comprehensive inspection train (referred to as CIT) is proposed. The identification of when maintenance activities occurred is reformulated as a model selection optimization problem based on Bayesian Information Criterion. An efficient solution algorithm utilizing adaptive thresholding and dynamic programming is proposed for solving this optimization problem. This framework’s validity and practicability are illustrated by the measurement data from the CIT inspection of the mileage section of K21 + 184 to K220 + 308 on the Nanchang-Fuzhou railway track from 2014 to 2019. The results indicate that this framework can overcome the disturbance of contaminated measurement data and accurately estimate when maintenance activities were undertaken without any historical maintenance records. What is more, the adaptive piecewise fitting model provided by this framework can describe the irregular deterioration process of corresponding rail track sections.

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“…Based on the track geometrical parameters, the track alignment, longitudinal level and track irregularity of different wave can be calculated by those inspecting instruments. The track irregularity is the key of evaluation index for deformations of track [14]. Because of some new characters in high-speed railway such as large radius of track curve, denseness and large quantity of track measuring points and small deviation between track measuring point and designed midline point, the accuracy and efficiency of lateral deviation for inspection of track irregularity in high-speed railway is more rigorous.…”

Section: Introductionmentioning

confidence: 99%

Study on Methods of Lateral Deviation of Track for Static Inspecting in High-Speed Railway

Cen

Bai

2014

AMM

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According to the critical factor of railway track geometric irregular, the deformations of track can be controlled. The lateral deviation is the key to determine the railway track irregularity. Currently, in the static inspected methods, the combined method of geodetic surveys and track surveying trolleys (inspecting instruments for static geometry parameter of track) is widely used in high-speed railway. Depending on some tests, the model of track irregularity in track surveying trolleys can be reconstructed by another method. According to the special features of track of high-speed railway, it is necessary to study on the accurate and effective lateral deviation algorithm which is suitable for the precise track inspection of high-speed railway. Based on some existing methods of construction layout in highway, the primary contents of this paper are: (1) reduces those methods to three algorithms, such as the Longitudinal Deviation Algorithm with Composite Simpson rule (LDACS), the Distance Function Algorithm of Newton’s method (DFAN) and Normal Perpendicular to Tangent Algorithm of Newton’s method (NPTAN), and (2) completes the algorithm steps of DFAN and NPTAN on circular curve, and proves the results of two algorithms on circular curve same, and (3) proposes the three algorithms to calculate lateral deviation and mileage of any rail detection points for inspecting the static geometric state of track in high-speed railway. Depending on some simulation data, the experimental results are: (1) the calculations of DFAN, NPTAN and LDACS, in which the number of subintervals of equal greater than or equal to five, meet the accuracy of the precise track detection of high-speed railway, and (2) the difference mileage and lateral deviation between DFAN and NPTAN are less than 0.001 mm, and (3) the efficiency of those algorithms is very considerable and the efficiency of DFAN is basically the same with NPTAN and higher than LDACS, and (4) the longer the transition curve is, the lower the efficiency and accuracy of DFAN and NPTAN are. The bigger the radius is, the higher the accuracy of LDACS is. According to the measurement data of the Chengdu Dujiangyan Railway Line (Cheng Guan Line), the above mentioned results of (1) and (2) can be proved correctly.

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A Novel Description Method for Track Irregularity Evolution

Cited by 7 publications

References 11 publications

Track geometry degradation and maintenance modelling: A review

Track geometry degradation and maintenance modelling: A review

BIC-Based Data-Driven Rail Track Deterioration Adaptive Piecewise Modeling Framework

Study on Methods of Lateral Deviation of Track for Static Inspecting in High-Speed Railway

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