Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach

Sysyn, Mykola; Przybyłowicz, Michał; Nabochenko, Olga; Kou, Lei

doi:10.3390/s21113609

Cited by 32 publications

(29 citation statements)

References 85 publications

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“…The track parameters are as follows: rail meter mass 60 kg/m, rail bending stiffness 6. Figure 5 The mechanical model of track and vehicle interaction in the void zone [44] The present study, different to the previous studies [13,44], is directed to the research of the sleeper-ballast interaction. The model parameters are fitted to the experimental measurement (Fig.…”

Section: Numerical Simulation Of the Dynamic Impactmentioning

confidence: 99%

“…Additionally, a simple method for the local track stiffness identification is proposed. An approach for track-side and on-board sleeper void identification and classification is proposed in the study [44]. The basic idea of the study lies on the especial pattern of dynamic impact in the void zone.…”

Section: Review Of the Literaturementioning

confidence: 99%

“…The previous research [43,44] have shown that the dynamic behaviour of the track with voids is depending of many factors like void sizes, train loadings and velocities, axle distances etc. The studies considered the dynamic behaviour for the usual train loadings and velocities.…”

Section: Experimental Measurements and Analysis Of The Void Dynamic Behaviour In A Void Zonementioning

confidence: 99%

“…The properties are bi-linear functions that provide the same elastic and damping parameters as in the outside zones after void closing. The more detailed description of the model is presented in the study [44], where the patterns of rail deflection and accelerations in the void zones are studied. The model parameters in the present study are selected to correspond to the post train from the experimental measurements.…”

Section: Numerical Simulation Of the Dynamic Impactmentioning

confidence: 99%

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Mechanism of the Sleeper-Ballast Dynamic Impact in Void Zones

Sysyn¹,

Przybyłowicz²,

Nabochenko³

et al. 2021

Preprint

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Unsupported sleepers or void zones in ballasted tracks are one of the most recent and frequent track failures. The void failures have the property of intensive development that, without timely maintenance measures, can cause the appearance of cost-expensive local instabilities like subgrade damages. The reason of the intensive void development lies in the mechanics of the sleeper and ballast bed interaction. The particularity of the interaction is a dynamic impact that occur due to void closure. Additionally, void zones cause inhomogeneous ballast pressure distribution between the void zone and fully supported neighbour zones. The present paper is devoted studying the mechanism of the sleeper-ballast dynamic impact in the void zone. The results of experimental in-situ measurements of rail deflections showed the significant impact accelerations in the zone even for light-weight slow vehicles. A simple 3-beam numerical model of track and rolling stock interaction has shown the similar to the experimental measurements dynamic interaction. Moreover, the model shows that the sleeper accelerations are more than 3 times higher than the corresponding wheel accelerations and the impact point appear before the wheel enters the impact point. The analysis of ballast loadings shows the specific impact behaviour in combination with the quasistatic part that is different for void and neighbour zones, which are characterised with high ballast pre-stressed conditions. The analysis of void sizes influence demonstrate that the impact loadings, wheel and sleeper maximal accelerations appear at certain void depth after which the values decrease. The ballast quasistatic loading analysis indicates more than twice increase of the ballast loading in neighbour zones for long voids and almost full quasistatic unloading for short length voids. However, the used imitation model cannot explain the nature of the dynamic impact. The mechanism of the void impact is clearly explained by the analytic solution using a simple clamped beam. A simplified analytical expression of the void impact velocity shows that it is linearly related to the wheel speed and loading. The comparison to the numerically simulated impact velocities shows a good agreement and the existence of the void depth with the maximal impact. An estimation of the long-term influences for the cases of normal sleeper loading, high ballast pre-stress and quasistatic loading in the neighbour zones and high impact inside the void are performed.

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Section: Numerical Simulation Of the Dynamic Impactmentioning

confidence: 99%

Section: Review Of the Literaturementioning

confidence: 99%

Section: Experimental Measurements and Analysis Of The Void Dynamic Behaviour In A Void Zonementioning

confidence: 99%

Section: Numerical Simulation Of the Dynamic Impactmentioning

confidence: 99%

See 2 more Smart Citations

Mechanism of the Sleeper-Ballast Dynamic Impact in Void Zones

Sysyn¹,

Przybyłowicz²,

Nabochenko³

et al. 2021

Preprint

Self Cite

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show abstract

“…Its deterioration affects the track geometry which results in lower passenger comfort, safety, and higher maintenance cost. The settlement of the ballast layer starts from voids 1 which causes excess vibration and high dynamic loading. Because sleepers are fastened with tracks, they will not settle along with the ballast layer.…”

Section: Introductionmentioning

confidence: 99%

Prognostics of unsupported railway sleepers and their severity diagnostics using machine learning

Sresakoolchai

Kaewunruen

2022

Sci Rep

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Railway sleepers are safety–critical components of a railway structure. They support ballasted track superstructure and are a critical factor in track geometry and track components’ deterioration. Unsupported sleepers are a common issue incurred after tracks have been utilized. When unsupported sleepers are present, they cause differential settlements of track superstructures, additional dynamic loading, and excessive train-track vibrations which affect passenger comfort, safety, and maintenance cost. This study is the world's first to develop new machine learning models to prognose and better diagnose defect severities of unsupported sleepers aligned with practical track inspection guidelines. Data used to develop machine learning models are based on a verified finite element model with actual field measurements, enabling unbiased full data ranges that govern all defect conditions. Different conditions of unsupported sleepers can be explored by varying locations of unsupported sleepers and the number of unsupported sleepers. Also, other operational parameters can be addressed such as speeds of rolling stock, the roughness of rail surface, and vertical stiffness of wheel-rail contact. In total, 2016 data sets have been obtained. Axle box accelerations are adopted as key indicators for machine learning models. Machine learning techniques used in the study are the convolutional neural network, recurrent neural network, ResNet, and fully convolutional neural network. Data fusion and assimilation have been conducted since the data points are dependent on the train speeds. Our new results reveal a breakthrough essential for real-world applications that the convolutional neural network model provides the best accuracy in both unsupported sleeper prognostics and severity identification. The accuracies of detection and severity identification are 99.34% and 97.02% respectively.

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Methods for Analyzing Combinations of Wheelset Sizes and Switch Elements

Korolev

2022

Lecture Notes in Networks and Systems

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Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach

Cited by 32 publications

References 85 publications

Mechanism of the Sleeper-Ballast Dynamic Impact in Void Zones

Mechanism of the Sleeper-Ballast Dynamic Impact in Void Zones

Prognostics of unsupported railway sleepers and their severity diagnostics using machine learning

Methods for Analyzing Combinations of Wheelset Sizes and Switch Elements

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