TY - GEN
T1 - A hybrid model for damage localization and prognosis including temperature compensation
AU - Neerukatti, Rajesh Kumar
AU - Hensberry, Kevin
AU - Kovvali, Narayan
AU - Chattopadhyay, Aditi
N1 - Funding Information:
This research is supported by the U.S. Department of Defense, U.S. Air Force Office of Scientific Research Multidisciplinary University Research Initiative Grant, FA95550-06-1-0309, Technical Monitor Dr. David Stargel.
PY - 2015
Y1 - 2015
N2 - The development of a reliable structural damage prognostics framework, which can accurately predict the fatigue life of critical metallic components subjected to a variety of in-service loading conditions, is important for many engineering applications. In this paper a hybrid damage localization method is developed for prediction of cracks in aluminum components. The proposed fully probabilistic methodology combines a physics based prognosis model with a data driven localization approach to estimate the crack growth. Particle filtering is used to iteratively combine the predicted crack location from prognostic model with the estimated crack location from localization algorithm to probabilistically estimate the crack location at each time instant, while accounting for the uncertainties. At each time step, the crack location predicted by the prognosis model is used as a priori knowledge (dynamic prior) and combined with the likelihood function of the localization algorithm for accurate crack location estimation. For improving the robustness of the localization framework, temperature compensation is carried out. The model is validated using experimental data obtained from fatigue tests preformed on an Al2024-T351 lug joint at different temperatures. The results indicate that the proposed method is capable of estimating the crack length with an error of less than 1mm for the majority of the presented cases.
AB - The development of a reliable structural damage prognostics framework, which can accurately predict the fatigue life of critical metallic components subjected to a variety of in-service loading conditions, is important for many engineering applications. In this paper a hybrid damage localization method is developed for prediction of cracks in aluminum components. The proposed fully probabilistic methodology combines a physics based prognosis model with a data driven localization approach to estimate the crack growth. Particle filtering is used to iteratively combine the predicted crack location from prognostic model with the estimated crack location from localization algorithm to probabilistically estimate the crack location at each time instant, while accounting for the uncertainties. At each time step, the crack location predicted by the prognosis model is used as a priori knowledge (dynamic prior) and combined with the likelihood function of the localization algorithm for accurate crack location estimation. For improving the robustness of the localization framework, temperature compensation is carried out. The model is validated using experimental data obtained from fatigue tests preformed on an Al2024-T351 lug joint at different temperatures. The results indicate that the proposed method is capable of estimating the crack length with an error of less than 1mm for the majority of the presented cases.
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M3 - Conference contribution
AN - SCOPUS:84987677715
T3 - International SAMPE Technical Conference
BT - SAMPE Baltimore 2015 Conference and Exhibition
PB - Soc. for the Advancement of Material and Process Engineering
T2 - SAMPE Baltimore 2015 Conference and Exhibition
Y2 - 18 May 2015 through 21 May 2015
ER -