Probabilistic Structural Health Monitoring Using Acoustic Emission
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Abstract
Fatigue crack initiation and growth during the service of aging aircraft are important life- limiting phenomena. In a previous study, a risk prediction and reliability model for naval aircraft has been developed based on fracture mechanics and inspection field data. Despite significant achievements in the study of fatigue cracks using fracture mechanics, it is still of great interest to find practical techniques for monitoring the crack growth using non-destructive inspection and to integrate the inspection results with the fracture mechanics models to improve the predictions. In this paper, a probabilistic damage-tolerance model based on acoustic emission monitoring is proposed to enhance the reliability and risk prediction for structures subject to fatigue cracking. Experiments were carried out to estimate the stress intensity range ∆K, during fatigue crack propagation using acoustic emission (AE) inspection. The uncertainty of parameters is captured via probability distribution functions. Bayesian regression technique was used to estimate the marginal and joint probability distributions of model parameters. Finally, an AE-based risk factor is defined as the probability of transitioning from stage II to stage III of fatigue crack growth regime. This transition probability is calculated as the probability that the maximum stress intensity exceeds the fracture toughness of the material at a given point in time, based on the AE inspection results.
How to Cite
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crack detection, damage detection, damage propagation model, data driven prognostics, fatigue crack growth, materials damage prognostics, structural health managemen, structural health monitoring, applications: aviation
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