Estimating the marginal maintenance cost of different vehicle types on rail infrastructure

Smith, Andrew; Odolinski, Kristofer; Nia, Saeed Hossein; Jönsson, Per-Anders; Stichel, Sebastian; Iwnicki, Simon; Wheat, Phill

doi:10.1177/0954409721991309

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…The econometric results indicated that TS is the most expensive of the three mechanisms in terms of the maintenance cost, followed by RCF and AW. Öberg and Andersson (2009) developed a bottom-up track charging model, considering TS, TCF, AW, and RCF in the context of Swedish mainline traffic and vehicles; additional similar studies have also been conducted (Smith et al, 2015(Smith et al, , 2016.…”

Section: Quantification Of Track Deteriorationmentioning

confidence: 99%

“…However, the abnormal interaction between rolling stock and infrastructure, which could be caused by RSFs such as wheel flats, brake failure, and suspension faults, was not considered. More information concerning the influence of WR interface on track deterioration can be found in (Smith, 2003;Falomi et al, 2011;Halama et al, 2011;Liang et al, 2013;Ye et al, 2020a).…”

Section: Quantification Of Track Deteriorationmentioning

confidence: 99%

“…In the rail vehicle sector, damage caused in this way is referred to as RCF. According to Burstow (2003) and Smith et al (2016), the RCF damage index on the rail surface is also calculated based on the Tγ value, as shown in Fig. 7.…”

Section: Rolling Contact Fatigue (Rcf)mentioning

confidence: 99%

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Quantification of the influence of rolling stock failures on track deterioration

Shi

Poveda-Reyes

et al. 2020

J. Zhejiang Univ. Sci. A

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This study focuses on the quantification of the influence of rolling stock failures (RSFs) on railway infrastructure. Taking the wheel flat, a common RSF, as an example, we introduce four quantification indexes to evaluate the influence on the following four deterioration mechanisms: track settlement (TS), track component fatigue (TCF), abrasive wear (AW), and rolling contact fatigue (RCF). Our results indicate that TS, TCF, and AW increase sharply with the increase of the wheel flat length and the vehicle speed, and this increasing trend becomes more acute with the increase of the wheel flat length and the vehicle speed. At low speeds, RCF increases gradually as the wheel flat length increases; at high speeds, it increases sharply at first and then decreases gradually. The influence of the wheel flat on TCF and AW is the most obvious, followed by TS and RCF. These findings can help infrastructure managers (IMs) to better understand infrastructure conditions related to RSFs and can aid them in managing problems with vehicle abnormality in track access charging.

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Section: Quantification Of Track Deteriorationmentioning

confidence: 99%

Section: Quantification Of Track Deteriorationmentioning

confidence: 99%

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Quantification of the influence of rolling stock failures on track deterioration

Shi

Poveda-Reyes

et al. 2020

J. Zhejiang Univ. Sci. A

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“…Therefore, referring to Refs. [64,65], the track model is simplified as a co-running track with a form of 'rail ? track slab ?…”

Section: Vehicle-track Modelmentioning

confidence: 99%

Deep learning-based fault diagnostic network of high-speed train secondary suspension systems for immunity to track irregularities and wheel wear

Huang

Zhang

2021

Rail. Eng. Science

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Fault detection and isolation of high-speed train suspension systems is of critical importance to guarantee train running safety. Firstly, the existing methods concerning fault detection or isolation of train suspension systems are briefly reviewed and divided into two categories, i.e., model-based and data-driven approaches. The advantages and disadvantages of these two categories of approaches are briefly summarized. Secondly, a 1D convolution network-based fault diagnostic method for high-speed train suspension systems is designed. To improve the robustness of the method, a Gaussian white noise strategy (GWN-strategy) for immunity to track irregularities and an edge sample training strategy (EST-strategy) for immunity to wheel wear are proposed. The whole network is called GWN-EST-1DCNN method. Thirdly, to show the performance of this method, a multibody dynamics simulation model of a high-speed train is built to generate the lateral acceleration of a bogie frame corresponding to different track irregularities, wheel profiles, and secondary suspension faults. The simulated signals are then inputted into the diagnostic network, and the results show the correctness and superiority of the GWN-EST-1DCNN method. Finally, the 1DCNN method is further validated using tracking data of a CRH3 train running on a high-speed railway line.

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New strategy for solving multi-objective green four dimensional transportation problems under normal type-2 uncertain environment

Bind,

Rani,

Ebrahimnejad

et al. 2024

Engineering Applications of Artificial Intelligence

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Estimating the marginal maintenance cost of different vehicle types on rail infrastructure

Cited by 5 publications

References 4 publications

Quantification of the influence of rolling stock failures on track deterioration

Quantification of the influence of rolling stock failures on track deterioration

Deep learning-based fault diagnostic network of high-speed train secondary suspension systems for immunity to track irregularities and wheel wear

New strategy for solving multi-objective green four dimensional transportation problems under normal type-2 uncertain environment

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