TY - JOUR
T1 - Fatigue life prediction using hybrid prognosis for structural health monitoring
AU - Neerukatti, Rajesh Kumar
AU - Liu, Kuang C.
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 Initiation grant FA95550-06-1-0309, David Stargel, Technical Monitor.
PY - 2014/4
Y1 - 2014/4
N2 - Because metallic aircraft components are subject to a variety of in-service loading conditions, predicting their fatigue life has become a critical challenge. To address the failure mode mitigation of aircraft components and at the same time reduce the life-cycle costs of aerospace systems, a reliable prognostics framework is essential. In this paper, a hybrid prognosis model that accurately predicts the crack growth regime and the residual-useful-life estimate of aluminum components is developed. The methodology integrates physics-based modeling with a data-driven approach. Different types of loading conditions such as constant amplitude, random, and overload are investigated. The developed methodology is validated on an Al 2024-T351 lug joint under fatigue loading conditions. The results indicate that fusing the measured data and physics-based models improves the accuracy of prediction compared to a purely data-driven or physics-based approach.
AB - Because metallic aircraft components are subject to a variety of in-service loading conditions, predicting their fatigue life has become a critical challenge. To address the failure mode mitigation of aircraft components and at the same time reduce the life-cycle costs of aerospace systems, a reliable prognostics framework is essential. In this paper, a hybrid prognosis model that accurately predicts the crack growth regime and the residual-useful-life estimate of aluminum components is developed. The methodology integrates physics-based modeling with a data-driven approach. Different types of loading conditions such as constant amplitude, random, and overload are investigated. The developed methodology is validated on an Al 2024-T351 lug joint under fatigue loading conditions. The results indicate that fusing the measured data and physics-based models improves the accuracy of prediction compared to a purely data-driven or physics-based approach.
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U2 - 10.2514/1.I010094
DO - 10.2514/1.I010094
M3 - Article
AN - SCOPUS:84900319237
SN - 1542-9423
VL - 11
SP - 211
EP - 231
JO - Journal of Aerospace Information Systems
JF - Journal of Aerospace Information Systems
IS - 4
ER -