A knowledge-based prognostics framework for railway track geometry degradation

Chiachío, Juan and Chiachío, Manuel and Prescott, Darren and Andrews, John (2018) A knowledge-based prognostics framework for railway track geometry degradation. Reliability Engineering and System Safety, 181. pp. 127-141. ISSN 0951-8320 (https://doi.org/10.1016/j.ress.2018.07.004)

[thumbnail of Chiachio-etal-RESS-2018-A-knowledge-based-prognostics-framework-for-railway-track-geometry-degradation]
Preview
 Text. Filename: Chiachio_etal_RESS_2018_A_knowledge_based_prognostics_framework_for_railway_track_geometry_degradation.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (3MB)| Preview

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.

ORCID iDs

Chiachío, Juan ORCID logoORCID: https://orcid.org/0000-0003-1243-8694, Chiachío, Manuel, Prescott, Darren and Andrews, John;
CORE (COnnecting REpositories)