A knowledge-based prognostics framework for railway track geometry degradation
Metadatos
Mostrar el registro completo del ítemEditorial
Elsevier BV
Materia
Railway track degradation Physics-based modelling Prognostics Particle filtering
Fecha
2018-07-05Referencia bibliográfica
Chiachío, J., Chiachío, M., Prescott, D., & Andrews, J. (2019). A knowledge-based prognostics framework for railway track geometry degradation. Reliability Engineering & System Safety, 181, 127-141.
Patrocinador
This work was supported by the Engineering and Physical Sciences Research Council [grant number EP/M023028/1]Resumen
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.