Hierarchical Bayesian modelling of rail track geometry degradation

Andrade, António R.; Teixeira, Paulo Fonseca

doi:10.1177/0954409713486619

Cited by 51 publications

(26 citation statements)

References 18 publications

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“…They present, though, the drawback of needing some maintenance and being quite delicate in a not friendly envirnoment [7]. They are also suitable for implementation in railway track degradation predictive models, as a useful indicator of the tracks maintenance conditions [8,9].…”

Section: Introductionmentioning

confidence: 99%

Axlebox accelerations: Their acquisition and time–frequency characterisation for railway track monitoring purposes

et al. 2016

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Railway track maintenance is becoming a real challenge for Railway Engineers due to the need of meeting increasingly high quality requirements by means of cost-effective procedures. Frequently, this can be only achieved by implementing some technological developments from other fields into the railway sector, such as Digital Signal Processing. Indeed, the present work delves into data acquisition and processing techniques in order to enhance track surveying processes. For this purpose, run tests on the Metropolitan Rail Network of Valencia (Spain) were carried out, and axlebox accelerations were gathered and analysed in different ways. The results determined the optimal sampling and filtering frequencies as well as the location of accelerometers along the train. Furthermore, by means of spectral analysis and time-frequency representations, diverse track defects, track singularities and vibration modes can be clearly identified. It is shown how, with a Hamming time window of 0.5 s and an overlapping of 95%, a wide set of track defects can be detected, without the need of complementary analyses. These values yield the best results as they are a good compromise between time and frequency resolution and allow for appropriate pattern recognition of the corresponding track singularities and resonant frequencies.

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Section: Introductionmentioning

confidence: 99%

Axlebox accelerations: Their acquisition and time–frequency characterisation for railway track monitoring purposes

et al. 2016

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“…Pavement deterioration was characterised using a Markov hazard model and Bayesian estimation. Andrade and Teixeira (2013) presented a hierarchical Bayesian model to predict rail track geometry degradation, which was quantified in terms of longitudinal-level defects, and thus guided planning maintenance and renewal actions.…”

Section: Literature Reviewmentioning

confidence: 99%

Renewal Management Framework for Urban Rail Transit Assets

Bai

Liu

et al. 2019

Transport

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Decision-making surrounding asset renewal is essential for the efficient use of renewal resources and safe operation of urban rail transit. In this study, major problems in the current management of urban rail industries in countries with the same problems as those in China were analysed, and in response, a renewal management framework based on service life estimation was proposed to provide adequate decision-making support for urban rail transit assets. In this framework, the cumulative failure frequency of an asset is used to indicate its health condition, and considering the uncertainties and heterogeneities in the deterioration process of assets, a Poisson–Weibull process model-based methodology was developed to estimate the service and residual lives of each asset, which are then employed in analysing its renewal demand and renewal period. Finally, the model is validated through an empirical study of rail renewal in the Beijing Metro. Our evaluation demonstrates that the proposed framework can estimate each asset’s service life accurately and can be used by asset management personnel to establish reasonable renewal plans and provide decision-making support for a scientifically informed resource allocation, thus mitigating major problems in current management practices.

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“…If the AL is passed, the condition of the track geometry should be regularly analysed and planned maintenance should be performed. 66 When a track section exceeds a predetermined degradation level, more inspections are required. A degraded track section is more vulnerable to defects; thus, if a certain degradation threshold is passed, immediate maintenance should be performed and modifications made.…”

Section: Track Geometry Maintenance Modelsmentioning

confidence: 99%

“…Andrade and Teixeira [64][65][66] To capture the section-to-section variation in geometry degradation the tamping efficiencies within sections were lying on a neighbourhood of a bad cluster. They also studied the effect of ballast age on tamping efficiency and found no significant relationship between tamping efficiency and ballast age.…”

Section: And Guo and Han 62mentioning

confidence: 99%

Track geometry degradation and maintenance modelling: A review

Soleimanmeigouni

Ahmadi

Kumar

2016

Proceedings of the Institution of Mechanical Engineers, Part F:

123

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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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Hierarchical Bayesian modelling of rail track geometry degradation

Cited by 51 publications

References 18 publications

Axlebox accelerations: Their acquisition and time–frequency characterisation for railway track monitoring purposes

Axlebox accelerations: Their acquisition and time–frequency characterisation for railway track monitoring purposes

Renewal Management Framework for Urban Rail Transit Assets

Track geometry degradation and maintenance modelling: A review

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