The work presented in this paper is focused on monitoring fatigue crack growth in metallic structures using acoustic emission (AE) technology. Three different methods are proposed to utilize the information obtained from in-situ monitoring for structural health management.
Fatigue crack growth tests with real-time acoustic emissions monitoring are conducted on CT specimens made of 7075 aluminum. Proper filtration of the resulting AE signals reveals a log-linear relationship between fracture parameters ( da⁄dN and ΔK ) and select AE features; a flexible statistical model is developed to describe the relationship between these parameters.Bayesian inference is used to estimate the model parameters from experimental data. The model is then used to calculate two important quantities that can be used for structural health management: (a) an AE-based instantaneous damage severity index, and (b) an AE-based estimate of the crack size distribution at a given point in time, assuming a known initial crack size distribution.
Finally, recursive Bayesian estimation is used for online integration of the structural health assessment information obtained from AE monitoring with crack size estimates obtained from empirical crack growth model. The evidence used in Bayesian updating includes observed crack sizes and/or crack growth rate observations
How to Cite
fatigue crack growth, structural health management, acoustic emission, Bayesian inference, fusion
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