Ensemble of Models for Fatigue Crack Growth Prognostics

Abstract

Various models of fatigue crack growth in different scenarios have been proposed in the literature. Here, in this paper, we propose a general prognostic framework for tracking crack evolution in equipment undergoing fatigue and predicting the Remaining Useful Life (RUL). The main contribution of this work is to integrate Particle Filtering (PF) and a new ensemble model which combines diverse physical degradation models with respect to their accuracy performance in previous time steps, in order to maximize the overall prediction capability. To validate the effectiveness of the proposed framework, a case study concerning multiple fatigue crack growth degradations is extensively investigated.