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A knowledge-based prognostics framework for railway track geometry degradation

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  • Chiachío, Juan
  • Chiachío, Manuel
  • Prescott, Darren
  • Andrews, John

Abstract

This paper proposes a paradigm shift to the problem of infrastructure asset management modelling by focusing towards forecasting the future condition of the assets instead of using empirical modelling approaches based on historical data. The proposed prognostics methodology is general but, in this paper, it is applied to the particular problem of railway track geometry deterioration due to its important implications in the safety and the maintenance costs of the overall infrastructure. As a key contribution, a knowledge-based prognostics approach is developed by fusing on-line data for track settlement with a physics-based model for track degradation within a filtering-based prognostics algorithm. The suitability of the proposed methodology is demonstrated and discussed in a case study using published data taken from a laboratory simulation of railway track settlement under cyclic loads, carried out at the University of Nottingham (UK). The results show that the proposed methodology is able to provide accurate predictions of the remaining useful life of the system after a model training period of about 10% of the process lifespan.

Suggested Citation

  • Chiachío, Juan & Chiachío, Manuel & Prescott, Darren & Andrews, John, 2019. "A knowledge-based prognostics framework for railway track geometry degradation," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 127-141.
    • Handle: RePEc:eee:reensy:v:181:y:2019:i:c:p:127-141
    DOI: 10.1016/j.ress.2018.07.004
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    References listed on IDEAS

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    1. An, Dawn & Kim, Nam H. & Choi, Joo-Ho, 2015. "Practical options for selecting data-driven or physics-based prognostics algorithms with reviews," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 223-236.
    2. Chiachío, Juan & Chiachío, Manuel & Sankararaman, Shankar & Saxena, Abhinav & Goebel, Kai, 2015. "Condition-based prediction of time-dependent reliability in composites," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 134-147.
    3. Chiachío, Manuel & Chiachío, Juan & Sankararaman, Shankar & Goebel, Kai & Andrews, John, 2017. "A new algorithm for prognostics using Subset Simulation," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 189-199.
    4. Andrews, John & Prescott, Darren & De Rozières, Florian, 2014. "A stochastic model for railway track asset management," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 76-84.
    5. Vale, Cecília & M. Lurdes, Simões, 2013. "Stochastic model for the geometrical rail track degradation process in the Portuguese railway Northern Line," Reliability Engineering and System Safety, Elsevier, vol. 116(C), pages 91-98.
    6. 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.
    7. L M Quiroga & E Schnieder, 2012. "Monte Carlo simulation of railway track geometry deterioration and restoration," Journal of Risk and Reliability, , vol. 226(3), pages 274-282, June.
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    Cited by:

    1. Chiachío, Juan & Jalón, María L. & Chiachío, Manuel & Kolios, Athanasios, 2020. "A Markov chains prognostics framework for complex degradation processes," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    2. Sedghi, Mahdieh & Kauppila, Osmo & Bergquist, Bjarne & Vanhatalo, Erik & Kulahci, Murat, 2021. "A taxonomy of railway track maintenance planning and scheduling: A review and research trends," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    3. Liu, Xinyang & Zheng, Zhuoyuan & Büyüktahtakın, İ. Esra & Zhou, Zhi & Wang, Pingfeng, 2021. "Battery asset management with cycle life prognosis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    4. 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).
    5. 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).
    6. 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).

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