A knowledge-based prognostics framework for railway track geometry degradation

Highlights: • Physics-based models and data are fused for railway track degradation forecasting. • A filtering-based prognostics methodology is adopted to deal with uncertainties. • A case study is presented using published data from Nottingham Railway Test Facility. • A new prognostics metric is proposed to compare the performance of different models. • Discussion is provided about extension to infrastructure asset management. -- 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.