A knowledge-based prognostics framework for railway track geometry degradation

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.

• Record URL:
  • https://doi.org/10.1016/j.ress.2018.07.004
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S095183201731400X
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09518320
• Supplemental Notes:
  • © 2018 Juan Chiachío et al.
• Authors:
  • Chiachío, Juan
  • Chiachío, Manuel
  • Prescott, Darren
  • Andrews, John
• Publication Date: 2019-1

Language

• English

Media Info

• Media Type: Digital/other
• Features: Appendices; Figures; References; Tables;
• Pagination: pp 127-141
• Serial:
  • Reliability Engineering & System Safety
  • Volume: 181
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0951-8320
  • Serial URL: https://www.sciencedirect.com/journal/reliability-engineering-and-system-safety

Subject/Index Terms

• TRT Terms: Data fusion; Expert systems; Predictive models; Railroad tracks; Service life; Structural deterioration and defects
• Subject Areas: Maintenance and Preservation; Planning and Forecasting; Railroads;

Filing Info

• Accession Number: 01839660
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 23 2022 10:53AM