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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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conferencepaper
Probabilistic method for damage identification in multi-layered composite structures
Abstract
The barely visible damages sustained in multi-layered composites can severely jeopardise the structural integrity and operational safety in real-life applications. Traditional intrusive inspections in condition monitoring can signicantly contribute to the cost and time overhead of such operation and is susceptible to errors when relying on manual inspection work ow. Acoustic emission (AE) techniques have received increasing attention in recent years for complex composite structures under service loads. AE is based on detecting acoustic energy emitted from damages sustained in structures (such as fatigue fracture, bre breakage, amongst others). The AE monitoring technique requires solving an inverse problem where the measured signals are linked to the source and nature of damage developed in the structure. However, given the signicant uncertainty around all real-life measurements of structures under operating loads, such as sporadic signals from multiple sources, re ection from boundaries or irregular geometric interfaces and measurement noise, it is essential to explicitly account for these uncertainties in the damage identication algorithms. The current work framework of automated probabilistic damage detection which explicitly models the parameterized uncertainties and conditions them based on measurement data to give probabilistic descriptors of damage metrics. The empirical relationship modelling the AE as a function of damage properties is calibrated with a training dataset. During the online monitoring phase, the spatially correlated time data is utilized in conjunction with the calibrated AE empirical 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 bre panel with stieners subjected to impact and dynamic fatigue loading. The study presents a generalized automated AE- based damage detection methodology which is applicable for structures with dierent ...