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.
The work presented in this paper provides an insight into the current challenges to detect incipient damage in complex metallic structural components. The goal of this research is to improve the confidence level in diagnosis and damage localization technologies by developing a robust structural health management (SHM) framework. Improved methodologies are developed for reference-free localization of fatigue induced cracks in complex metallic structures. The methodologies for damage interrogation involve damage feature extraction using advanced signal processing tools and a probabilistic approach for damage detection and localization. Specifically, piezoelectric transducers are used in pitch-catch mode to interrogate the structure with guided Lamb waves. A novel time-frequency (TF) based signal processing technique based on the matching pursuit decomposition (MPD) algorithm is developed to extract time-of-flight damage features from dispersive guided wave sensor signals, followed by a Bayesian probabilistic approach used to optimally fuse multi-sensor information and localize the crack tip. The MPD algorithm decomposes a signal using localized TF atoms and can provide a highly concentrated TF representation. The Bayesian probabilistic framework enables the effective quantification and management of uncertainty. Experiments are conducted to validate the proposed detection and localization methods. Results presented will illustrate the usefulness of the developed approaches in detection and localization of damage in aluminum lug joints.
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