Methodologies for prognostics usually centre on physics-based or data-driven approaches. Both have advantages and disadvantages, but accurate prediction relies on extensive data being available. For industrial applications this is very rarely the case, and hence the chosen methodβs performance can deteriorate quite markedly from optimal. For this reason a hybrid methodology, merging physics-based and data-driven approaches, has been developed and is reported here. Most, if not all, hybrid methods apply physics-based and data-driven approaches in different steps of the prognostics process (i.e. state estimation and state forecasting). The presented technique combines both methods in forecasting, and integrates the short-term prediction of a physics-based model with the longer term projection of a similarity-based data-driven model, to obtain remaining useful life estimation. The proposed hybrid prognostic methodology has been tested on two engineering datasets, one for crack growth and the other for filter clogging. The performance of the presented methodology has been evaluated by comparing remaining useful life estimations obtained from both hybrid and individual prognostic models. The results show that the presented methodology improves accuracy, robustness and applicability, especially in the case of minimal data being available
