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Probabilistic prognosis of fatigue crack growth using acoustic emission data / Boris A. Zarate in Journal of engineering mechanics, Vol. 138 N° 9 (Septembre 2012) 

Public ISBD [article]  in Journal of engineering mechanics > Vol. 138 N° 9 (Septembre 2012) . - pp.1101–1111. Titre : Probabilistic prognosis of fatigue crack growth using acoustic emission data Type de document : texte imprimé Auteurs : Boris A. Zarate, Auteur ; Juan M. Caicedo, Auteur ; Jianguo Yu, Auteur Année de publication : 2012 Article en page(s) : pp.1101–1111. Note générale : Mécanique appliquée Langues : Anglais (eng) Mots-clés : Structural  health  monitoring  Fatigue  crack  growth  Acoustic  emission  Model  updating  Bayesian  inference  Probabilistic  prognosis Résumé : This paper presents a structural health monitoring methodology that uses acoustic emission (AE) features to predict crack growth in structural elements subjected to fatigue. This allows for the prediction of the failure of the structural element at the current load level. The methodology uses Bayesian inference to account for different sources of uncertainty such as uncertainty in the data (AE signal), unknown fracture mechanics parameters, and model inadequacy. The methodology is divided into two main components: a model updating component that uses available data to build a joint probability distribution of the different unknown fracture mechanics parameters, and a prognosis component in which this multivariable probability distribution is sampled to predict the stress intensity factor range at a future number of cycles. The application of the methodology does not require knowledge of the load amplitude nor the initial crack length. The methodology is validated using experimental data from a compact test specimen under cyclic loading. ISSN : 0733-9399 En ligne : http://ascelibrary.org/doi/abs/10.1061/%28ASCE%29EM.1943-7889.0000414 [article] Probabilistic prognosis of fatigue crack growth using acoustic emission data [texte imprimé] / Boris A. Zarate, Auteur ; Juan M. Caicedo, Auteur ; Jianguo Yu, Auteur . - 2012 . - pp.1101–1111. Mécanique appliquée Langues : Anglais (eng) in Journal of engineering mechanics > Vol. 138 N° 9 (Septembre 2012) . - pp.1101–1111. Mots-clés : Structural  health  monitoring  Fatigue  crack  growth  Acoustic  emission  Model  updating  Bayesian  inference  Probabilistic  prognosis Résumé : This paper presents a structural health monitoring methodology that uses acoustic emission (AE) features to predict crack growth in structural elements subjected to fatigue. This allows for the prediction of the failure of the structural element at the current load level. The methodology uses Bayesian inference to account for different sources of uncertainty such as uncertainty in the data (AE signal), unknown fracture mechanics parameters, and model inadequacy. The methodology is divided into two main components: a model updating component that uses available data to build a joint probability distribution of the different unknown fracture mechanics parameters, and a prognosis component in which this multivariable probability distribution is sampled to predict the stress intensity factor range at a future number of cycles. The application of the methodology does not require knowledge of the load amplitude nor the initial crack length. The methodology is validated using experimental data from a compact test specimen under cyclic loading. ISSN : 0733-9399 En ligne : http://ascelibrary.org/doi/abs/10.1061/%28ASCE%29EM.1943-7889.0000414