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Robust assessment of railway vehicle safety risks in operation using a proposed data-driven wheel profile generation approach: Design of computer experiments and surrogate models

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  • Braga, Joaquim A.P.
  • Costa, João N.
  • Ambrósio, Jorge
  • Frey, Daniel
  • Andrade, António R.

Abstract

Worldwide objectives for railway vehicles are increased capacity, faster travels and higher levels of safety. In the vehicle-track complex system, assessing and controlling the interactions between the wheels and the rail track is crucial to these goals. Wheel profiles are specifically designed to steer the vehicle and avoid derailment. Maintenance standards and train operating companies establish safe envelopes for wheel profile geometric parameters. A design of experiments is conducted to model relationships between allowable wheel parameters and expected vehicle safety risks, which is supported by condition monitoring data from operation. Such a robust assessment is missing in the literature. The applied methods consist of: (i) selection of predictors and pre-processing, based on literature, standards and a purely data-driven approach to generate wheel profiles; (ii) space-filling design, using Latin hypercube sampling; (iii) obtaining vehicle responses and post-processing, using a multibody dynamics commercial software and according to standards; (iv) surrogate modelling, using Gaussian processes and linear models; (v) sensitivity analysis, through Sobol indices; (vi) safety assessment, analysing response surfaces. Wheels with large flange height and thickness result in higher flange climb derailment risks. The proposed approach allows quantifying this risk as a function of profile parameters and mitigate it through maintenance actions.

Suggested Citation

  • Braga, Joaquim A.P. & Costa, João N. & Ambrósio, Jorge & Frey, Daniel & Andrade, António R., 2024. "Robust assessment of railway vehicle safety risks in operation using a proposed data-driven wheel profile generation approach: Design of computer experiments and surrogate models," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    • Handle: RePEc:eee:reensy:v:249:y:2024:i:c:s095183202400293x
    DOI: 10.1016/j.ress.2024.110220
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    References listed on IDEAS

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    1. Braga, Joaquim A.P. & Andrade, António R., 2021. "Multivariate statistical aggregation and dimensionality reduction techniques to improve monitoring and maintenance in railways: The wheelset component," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Joaquim AP Braga & António R Andrade, 2019. "Optimizing maintenance decisions in railway wheelsets: A Markov decision process approach," Journal of Risk and Reliability, , vol. 233(2), pages 285-300, April.
    3. Yang, Seonghyeok & Lee, Mingyu & Lee, Ikjin, 2023. "A new sampling approach for system reliability-based design optimization under multiple simulation models," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    4. Fumitoshi Mizutani & Shuji Uranishi, 2013. "Does vertical separation reduce cost? An empirical analysis of the rail industry in European and East Asian OECD Countries," Journal of Regulatory Economics, Springer, vol. 43(1), pages 31-59, January.
    5. Andrade, A.R. & Teixeira, P.F., 2015. "Statistical modelling of railway track geometry degradation using Hierarchical Bayesian models," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 169-183.
    6. Neves Costa, João & Ambrósio, Jorge & Andrade, António R. & Frey, Daniel, 2023. "Safety assessment using computer experiments and surrogate modeling: Railway vehicle safety and track quality indices," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    7. Lin, Penghui & Zhang, Limao & Tiong, Robert L.K., 2023. "Multi-objective robust optimization for enhanced safety in large-diameter tunnel construction with interactive and explainable AI," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    8. Ye, Yunguang & Huang, Caihong & Zeng, Jing & Wang, Suqin & Liu, Chaotao & Li, Fansong, 2023. "Predicting railway wheel wear by calibrating existing wear models: Principle and application," Reliability Engineering and System Safety, Elsevier, vol. 238(C).
    9. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    10. Alimohammadi, H.R. & Naseh, H. & Ommi, F., 2023. "An integrated methodology applied for reliability based multi-disciplinary design optimization in EPFE with LOX/kerosene," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    11. Jung, WoongHee & Taflanidis, Alexandros A., 2023. "Efficient global sensitivity analysis for high-dimensional outputs combining data-driven probability models and dimensionality reduction," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    12. Allahvirdizadeh, R. & Andersson, A. & Karoumi, R., 2023. "Improved dynamic design method of ballasted high-speed railway bridges using surrogate-assisted reliability-based design optimization of dependent variables," Reliability Engineering and System Safety, Elsevier, vol. 238(C).
    13. Saleh, Ali & Remenyte-Prescott, Rasa & Prescott, Darren & Chiachío, Manuel, 2024. "Intelligent and adaptive asset management model for railway sections using the iPN method," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    14. Andrade, Antonio Ramos & Stow, Julian, 2017. "Assessing the potential cost savings of introducing the maintenance option of ‘Economic Tyre Turning’ in Great Britain railway wheelsets," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 317-325.
    15. Shang, Yue & Nogal, Maria & Teixeira, Rui & Wolfert, A.R. (Rogier) M., 2024. "Extreme-oriented sensitivity analysis using sparse polynomial chaos expansion. Application to train–track–bridge systems," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
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