IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i24p16565-d999420.html
   My bibliography  Save this article

Evolution of Rail Contact Fatigue on Crossing Nose Rail Based on Long Short-Term Memory

Author

Listed:
  • Lei Kou

    (Institute of Railway Systems and Public Transport, Technical University Dresden, 01069 Dresden, Germany)

  • Mykola Sysyn

    (Institute of Railway Systems and Public Transport, Technical University Dresden, 01069 Dresden, Germany)

  • Jianxing Liu

    (School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Olga Nabochenko

    (Institute of Railway Systems and Public Transport, Technical University Dresden, 01069 Dresden, Germany)

  • Yue Han

    (China Railway Hohhot Group Co., Ltd., Hohhot 012000, China)

  • Dai Peng

    (Infrastructure Inspection Research Institute, China Academy of Railway Sciences, Beijing 100081, China)

  • Szabolcs Fischer

    (Department of Transport Infrastructure and Water Resources Engineering, Faculty of Architecture, Civil and Transport Engineering, Szechenyi Istvan University, 9026 Gyor, Hungary)

Abstract

The share of rail transport in world transport continues to rise. As the number of trains increases, so does the load on the railway. The rails are in direct contact with the loaded wheels. Therefore, it is more easily damaged. In recent years, domestic and foreign scholars have conducted in-depth research on railway damage detection. As the weakest part of the track system, switches are more prone to damage. Assessing and predicting rail surface damage can improve the safety of rail operations and allow for proper planning and maintenance to reduce capital expenditure and increase operational efficiency. Under the premise that functional safety is paramount, predicting the service life of rails, especially turnouts, can significantly reduce costs and ensure the safety of railway transportation. This paper understands the evolution of contact fatigue on crossing noses through long-term observation and sampling of crossing noses in turnouts. The authors get images from new to damaged. After image preprocessing, MPI (Magnetic Particle Imaging) is divided into blocks containing local crack information. The obtained local texture information is used for regression prediction using machine-supervised learning and LSTM network (Long Short-Term Memory) methods. Finally, a technique capable of thoroughly evaluating the wear process of crossing noses is proposed.

Suggested Citation

  • Lei Kou & Mykola Sysyn & Jianxing Liu & Olga Nabochenko & Yue Han & Dai Peng & Szabolcs Fischer, 2022. "Evolution of Rail Contact Fatigue on Crossing Nose Rail Based on Long Short-Term Memory," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:16565-:d:999420
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/24/16565/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/24/16565/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Colin Cameron, A. & Windmeijer, Frank A. G., 1997. "An R-squared measure of goodness of fit for some common nonlinear regression models," Journal of Econometrics, Elsevier, vol. 77(2), pages 329-342, April.
    2. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    3. Mohsen Momenitabar & Raj Bridgelall & Zhila Dehdari Ebrahimi & Mohammad Arani, 2021. "Literature Review of Socioeconomic and Environmental Impacts of High-Speed Rail in the World," Sustainability, MDPI, vol. 13(21), pages 1-27, November.
    4. Praneeth Chandran & Johnny Asber & Florian Thiery & Johan Odelius & Matti Rantatalo, 2021. "An Investigation of Railway Fastener Detection Using Image Processing and Augmented Deep Learning," Sustainability, MDPI, vol. 13(21), pages 1-15, October.
    5. Elahe Talebiahooie & Florian Thiery & Jingjing Meng & Hans Mattsson & Erling Nordlund & Matti Rantatalo, 2021. "Modelling of Railway Sleeper Settlement under Cyclic Loading Using a Hysteretic Ballast Contact Model," Sustainability, MDPI, vol. 13(21), pages 1-16, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fabrizio De Caro & Jacopo De Stefani & Gianluca Bontempi & Alfredo A. Vaccaro & Domenico D. Villacci, 2020. "Robust Assessment of Short-Term Wind Power Forecasting Models on Multiple Time Horizons," ULB Institutional Repository 2013/314435, ULB -- Universite Libre de Bruxelles.
    2. Moting Su & Zongyi Zhang & Ye Zhu & Donglan Zha, 2019. "Data-Driven Natural Gas Spot Price Forecasting with Least Squares Regression Boosting Algorithm," Energies, MDPI, vol. 12(6), pages 1-13, March.
    3. Moting Su & Zongyi Zhang & Ye Zhu & Donglan Zha & Wenying Wen, 2019. "Data Driven Natural Gas Spot Price Prediction Models Using Machine Learning Methods," Energies, MDPI, vol. 12(9), pages 1-17, May.
    4. repec:prg:jnlcfu:v:2022:y:2022:i:1:id:572 is not listed on IDEAS
    5. Ghazi Falah & Michael Hoy & Rakhal Sarker, 2000. "Co-existence in Selected Mixed Arab-Jewish Cities in Israel: By Choice or by Default?," Urban Studies, Urban Studies Journal Limited, vol. 37(4), pages 775-796, April.
    6. Cowell, Frank & Flachaire, Emmanuel & Bandyopadhyay, Sanghamitra, 2009. "Goodness-of-fit: an economic approach," LSE Research Online Documents on Economics 25433, London School of Economics and Political Science, LSE Library.
    7. Chang, Andrew C. & Hanson, Tyler J., 2016. "The accuracy of forecasts prepared for the Federal Open Market Committee," Journal of Economics and Business, Elsevier, vol. 83(C), pages 23-43.
    8. Ling Tang & Chengyuan Zhang & Tingfei Li & Ling Li, 2021. "A novel BEMD-based method for forecasting tourist volume with search engine data," Tourism Economics, , vol. 27(5), pages 1015-1038, August.
    9. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    10. Michael Vössing & Niklas Kühl & Matteo Lind & Gerhard Satzger, 2022. "Designing Transparency for Effective Human-AI Collaboration," Information Systems Frontiers, Springer, vol. 24(3), pages 877-895, June.
    11. Frank, Johannes, 2023. "Forecasting realized volatility in turbulent times using temporal fusion transformers," FAU Discussion Papers in Economics 03/2023, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    12. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    13. Jeon, Yunho & Seong, Sihyeon, 2022. "Robust recurrent network model for intermittent time-series forecasting," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1415-1425.
    14. Snyder, Ralph D. & Ord, J. Keith & Koehler, Anne B. & McLaren, Keith R. & Beaumont, Adrian N., 2017. "Forecasting compositional time series: A state space approach," International Journal of Forecasting, Elsevier, vol. 33(2), pages 502-512.
    15. Paulo Júlio & Pedro M. Esperança, 2012. "Evaluating the forecast quality of GDP components: An application to G7," GEE Papers 0047, Gabinete de Estratégia e Estudos, Ministério da Economia, revised Apr 2012.
    16. Rivera, Nilza & Guzmán, Juan Ignacio & Jara, José Joaquín & Lagos, Gustavo, 2021. "Evaluation of econometric models of secondary refined copper supply," Resources Policy, Elsevier, vol. 73(C).
    17. Cameron Roach & Rob Hyndman & Souhaib Ben Taieb, 2021. "Non‐linear mixed‐effects models for time series forecasting of smart meter demand," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1118-1130, September.
    18. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Effects on the Riskless Yield Curve with Regime Switching Nelson†Siegel Models," Working Papers 639, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    19. Li, Jiarong & Li, Xiangdong & Wang, Yong & Tu, Jiyuan, 2020. "A theoretical model of natural circulation flow and heat transfer within horizontal evacuated tube considering the secondary flow," Renewable Energy, Elsevier, vol. 147(P1), pages 630-638.
    20. Alysha M De Livera, 2010. "Automatic forecasting with a modified exponential smoothing state space framework," Monash Econometrics and Business Statistics Working Papers 10/10, Monash University, Department of Econometrics and Business Statistics.
    21. Philippe St-Aubin & Bruno Agard, 2022. "Precision and Reliability of Forecasts Performance Metrics," Forecasting, MDPI, vol. 4(4), pages 1-22, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:16565-:d:999420. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.