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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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Broer, Agnes A. R.
Delft University of Technology
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (11/11 displayed)
- 2023Intelligent Health Indicators Based on Semi-supervised Learning Utilizing Acoustic Emission Datacitations
- 2023Hierarchical Upscaling of Data-Driven Damage Diagnostics for Stiffened Composite Aircraft Structures
- 2023Intelligent health indicator construction for prognostics of composite structures utilizing a semi-supervised deep neural network and SHM datacitations
- 2023An SHM Data-Driven Methodology for the Remaining Useful Life Prognosis of Aeronautical Subcomponentscitations
- 2023A novel strain-based health indicator for the remaining useful life estimation of degrading composite structurescitations
- 2022On the Challenges of Upscaling Damage Monitoring Methodologies for Stiffened Composite Aircraft Panelscitations
- 2022Assessing stiffness degradation of stiffened composite panels in post-buckling compression-compression fatigue using guided wavescitations
- 2021A Strain-Based Health Indicator for the SHM of Skin-to-Stringer Disbond Growth of Composite Stiffened Panels in Fatiguecitations
- 2021Health monitoring of aerospace structures utilizing novel health indicators extracted from complex strain and acoustic emission datacitations
- 2021Fusion-based damage diagnostics for stiffened composite panelscitations
- 2021Health indicators for diagnostics and prognostics of composite aerospace structurescitations
Places of action
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article
Fusion-based damage diagnostics for stiffened composite panels
Abstract
Conducting damage diagnostics on stiffened panels is commonly performed using a single SHM technique. However, each SHM technique has both its strengths and limitations. Rather than straining the expansion of single SHM techniques going beyond their intrinsic capacities, these strengths and limitations should instead be considered in their application. In this work, we propose a novel fusion-based methodology between data from two SHM techniques in order to surpass the capabilities of a single SHM technique. The aim is to show that by considering data fusion, a synergy can be obtained, resulting in a comprehensive damage assessment, not possible using a single SHM technique. For this purpose, three single-stiffener carbon–epoxy panels were subjected to fatigue compression after impact tests. Two SHM techniques monitored damage growth under the applied fatigue loads: acoustic emission and distributed fiber optic strain sensing. Four acoustic emission sensors were placed on each panel, thereby allowing for damage detection, localization, type identification (delamination), and severity assessment. The optical fibers were adhered to the stiffener feet’ surface, and its strain measurements were used for damage detection, disbond localization, damage type identification (stiffness degradation and disbond growth), and severity assessment. Different fusion techniques are presented in order to integrate the acoustic emission and strain data. For damage detection and severity assessment, a hybrid health indicator is obtained by feature-level fusion while a complementary and cooperative fusion of the diagnostic results is developed for damage localization and type identification. We show that damage growth can be monitored up until final failure, thereby performing a simultaneous damage assessment on all four SHM levels. In this manner, we demonstrate that by proposing a fusion-based approach toward SHM of composite structures, the intrinsic capacity of each SHM technique can be utilized, leading to synergistic effects for damage diagnostics.