Dynamic-weighted ensemble for fatigue crack degradation state prediction

This paper proposes a prognostic framework for online prediction of fatigue crack growth in industrial equipment. The key contribution is the combination of a recursive Bayesian technique and a dynamic-weighted ensemble methodology to integrate multiple stochastic degradation models. To show the application of the proposed framework, a case study is considered, concerning fatigue crack growth under time-varying operation conditions. The results indicate that the proposed prognostic framework performs well in comparison to single crack growth models in terms of prediction accuracy under evolving operating conditions.