Quantitative methods for structural health management using in situ acoustic emission monitoring

This paper presents an end-to-end approach for structural health management using acoustic emission (AE) monitoring. Three quantitative methods are proposed to utilize the information obtained from in situ AE monitoring to improve structural integrity assessment. Fatigue crack growth tests with real-time acoustic emissions monitoring are conducted on CT specimens made of 7075-T6 aluminum. Proper filtration of the resulting AE signals reveals a log-linear relationship between fracture parameters (e.g. crack growth rate) 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 data used in Bayesian updating includes observed crack sizes and/or crack growth rate observations.

► Modeled the statistical relation between AE features and fatigue parameters.
► Bayesian regression is used to capture and characterize the uncertainty in modeling.
► A crack growth model based only on AE monitoring data is developed.
► Risk of transition to unstable crack growth predicted by comparing AE features with KIc.
► AE data used to update the output and the parameters of empirical crack growth model.