Validation of a Physics-based Prognostic Model with Incomplete Data

While the development of prognostic models is nowadays rather feasible, the implementation and validation thereof can still create many challenges. One of the main challenges is the lack of high-quality input data like operational data, environmental data, maintenance data and the limited amount of degradation or failure data. The uncertainty in the output of the prognostic model needs to be quantified before it can be utilised for either model validation or actual maintenance decision support. This study, therefore, proposes a generic framework for prognostic model validation with limited data based on uncertainty propagation. This is realised by using sensitivity indices, correlation coefficients, Monte Carlo simulations and analytical approaches. For demonstration purposes, a rail wear prognostic model is used. The demonstration concludes that by following the generic framework, the prognostic model can be validated, and as a result, realistic maintenance advice can be given to rail infrastructure managers, even when limited data is available.