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Article Dans Une Revue Engineering Fracture Mechanics Année : 2018

Dynamic-weighted ensemble for fatigue crack degradation state prediction

Résumé

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.
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Dates et versions

hal-01988976 , version 1 (22-01-2019)

Identifiants

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Hoang-Phuong Nguyen, Jie Liu, Enrico Zio. Dynamic-weighted ensemble for fatigue crack degradation state prediction. Engineering Fracture Mechanics, 2018, 194, pp.212-223. ⟨10.1016/j.engfracmech.2018.03.013⟩. ⟨hal-01988976⟩
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