Acoustic emission based damage localization in composites structures using Bayesian identification

12th International Conference on Damage Assessment of Structures

Kundu, A.^1 ; Eaton, M.J.^1 ; Al-Jumali, S.^2 ; Sikdar, S.^3 ; Pullin, R.^1

Cardiff School of Engineering, Cardiff University, Queen's Building, The Parade, Cardiff

CF24 3AA, United Kingdom^1

Materials Department, College of Engineering, University of Basrah, Basrah, Iraq^2

Institute of Fluid-Flow Machinery, Polish Academy of Sciences, 14, Fiszera Street, Gdansk

80-231, Poland^3

关键词: Acoustic emission sources;    Acoustic emission systems;    Bayesian identification;    Non-destructive monitoring;    Probabilistic damage detections;    Probabilistic descriptions;    Response surface modelling;    Structural damage detection;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/842/1/012081/pdf
DOI  :  10.1088/1742-6596/842/1/012081

来源: IOP

【 摘 要 】

Acoustic emission based damage detection in composite structures is based on detection of ultra high frequency packets of acoustic waves emitted from damage sources (such as fibre breakage, fatigue fracture, amongst others) with a network of distributed sensors. This non-destructive monitoring scheme requires solving an inverse problem where the measured signals are linked back to the location of the source. This in turn enables rapid deployment of mitigative measures. The presence of significant amount of uncertainty associated with the operating conditions and measurements makes the problem of damage identification quite challenging. The uncertainties stem from the fact that the measured signals are affected by the irregular geometries, manufacturing imprecision, imperfect boundary conditions, existing damages/structural degradation, amongst others. This work aims to tackle these uncertainties within a framework of automated probabilistic damage detection. The method trains a probabilistic model of the parametrized input and output model of the acoustic emission system with experimental data to give probabilistic descriptors of damage locations. A response surface modelling the acoustic emission as a function of parametrized damage signals collected from sensors would be calibrated with a training dataset using Bayesian inference. This is used to deduce damage locations in the online monitoring phase. During online monitoring, the spatially correlated time data is utilized in conjunction with the calibrated acoustic emissions model to infer the probabilistic description of the acoustic emission source within a hierarchical Bayesian inference framework. The methodology is tested on a composite structure consisting of carbon fibre panel with stiffeners and damage source behaviour has been experimentally simulated using standard H-N sources. The methodology presented in this study would be applicable in the current form to structural damage detection under varying operational loads and would be investigated in future studies.