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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Kundu, Abhishek
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (10/10 displayed)
- 2023Elastic modulus of self-compacting fibre reinforced concrete: Experimental approach and multi-scale simulationcitations
- 2023Deep learning for automatic assessment of breathing-debonds in stiffened composite panels using non-linear guided wave signalscitations
- 2022Acoustic emission data based deep learning approach for classification and detection of damage-sources in a composite panelcitations
- 2021A Gaussian Process Based Model for Air-Jet Cooling of Mild Steel Plate in Run Out Table
- 2019Nondestructive Analysis of Debonds in a Composite Structure under Variable Temperature Conditionscitations
- 2019Nondestructive analysis of debonds in a composite structure under variable temperature conditionscitations
- 2019A generic framework for application of machine learning in acoustic emission-based damage identificationcitations
- 2018Probabilistic method for damage identification in multi-layered composite structures
- 2018Online detection of barely visible low-speed impact damage in 3D-core sandwich composite structurecitations
- 2017Acoustic emission based damage localization in composites structures using Bayesian identificationcitations
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article
Acoustic emission based damage localization in composites structures using Bayesian identification
Abstract
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 ...