A knowledge-based prognostics framework for railway track geometry degradation

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.

Citation

Chiachío, J., Chiachío, M., Prescott, D., & Andrews, J. (2019). A knowledge-based prognostics framework for railway track geometry degradation. Reliability Engineering and System Safety, 181, 127-141. https://doi.org/10.1016/j.ress.2018.07.004

• Journal Article Type: Article
• Acceptance Date: Jul 4, 2018
• Online Publication Date: Jul 5, 2018
• Publication Date: Jan 1, 2019
• Deposit Date: Sep 21, 2018
• Publicly Available Date: Nov 1, 2018
• Journal: Reliability Engineering & System Safety
• Print ISSN: 0951-8320
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 181
• Pages: 127-141
• DOI: https://doi.org/10.1016/j.ress.2018.07.004
• Keywords: Railway track degradation; Physics-based modelling; Prognostics; Particle filtering
• Public URL: https://nottingham-repository.worktribe.com/output/1099013
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S095183201731400X