A novel probabilistic approach for damage localization and prognosis including temperature compensation

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 article, a novel integrated structural damage localization method is developed for prediction of cracks in aluminum components. The proposed methodology combines a physics-based prognosis model with a data-driven localization approach to estimate the crack growth. Specifically, 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. 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, online temperature estimation is carried out. The model is validated using experimental data obtained from fatigue tests preformed on an Al2024-T351 lug joint. The results indicate that the proposed method is capable of tracking the crack length with an error of less than 1 mm for the majority of the presented cases.