Fatigue Life Prediction Using Hybrid Prognosis for Structural Health Monitoring

A reliable prognostics framework is essential to address failure mode mitigation and life cycle cost of aerospace systems. Metallic aircraft components are subject to a variety of inservice loading conditions and prediction of fatigue life remains a critical challenge. A hybrid prognostic model, which can accurately predict the crack growth regime and the residual useful life estimate (RULE) of aluminum components, is developed to address this issue. This model uses an integrated technique; coupling physics based approach with a data-driven approach. Different types of loading such as constant amplitude, random and overload are considered and the developed methodology is validated with the experimental data available in the literature. The results indicate that fusing the measured data and physics based models improves the accuracy of prediction compared to a pure data-driven or physics based approach.