| People | Locations | Statistics |
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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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Zarouchas, Dimitrios
Delft University of Technology
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (30/30 displayed)
- 2024Innovative welding integration of acousto-ultrasonic composite transducers onto thermoplastic composite structurescitations
- 2023Intelligent Health Indicators Based on Semi-supervised Learning Utilizing Acoustic Emission Datacitations
- 2023Non-destructive strength prediction of composite laminates utilizing deep learning and the stochastic finite element methodscitations
- 2023Acousto-ultrasonic composite transducers integration into thermoplastic composite structures via ultrasonic welding
- 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
- 2023Developing health indicators for composite structures based on a two-stage semi-supervised machine learning model using acoustic emission datacitations
- 2023Analysis of Stochastic Matrix Crack Evolution in CFRP Cross-Ply Laminates under Fatigue Loadingcitations
- 2023Delamination Size Prediction for Compressive Fatigue Loaded Composite Structures Via Ultrasonic Guided Wave Based Structural Health Monitoring
- 2022On the Challenges of Upscaling Damage Monitoring Methodologies for Stiffened Composite Aircraft Panelscitations
- 2022Synthesis and characterization of novel eco-epoxy adhesives based on the modified tannic acid for self-healing jointscitations
- 2022Synthesis and characterization of novel eco-epoxy adhesives based on the modified tannic acid for self-healing jointscitations
- 2022Assessing stiffness degradation of stiffened composite panels in post-buckling compression-compression fatigue using guided wavescitations
- 2022Early fatigue damage accumulation of CFRP Cross-Ply laminates considering size and stress level effectscitations
- 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
- 2021A review of experimental and theoretical fracture characterization of bi-material bonded jointscitations
- 2021Fusion-based damage diagnostics for stiffened composite panelscitations
- 2021Health indicators for diagnostics and prognostics of composite aerospace structurescitations
- 2021Damage assessment of a titanium skin adhesively bonded to carbon fiber–reinforced plastic omega stringers using acoustic emissioncitations
- 2020Damage assessment of NCF, 2D and 3D Woven Composites under Compression After Multiple-Impact using Acoustic Emissioncitations
- 2020The effect of temperature on fatigue strength of poly(ether-imide)/multiwalled carbon nanotube/carbon fibers composites for aeronautical applicationcitations
- 2019Compression After Multiple Low Velocity Impacts of NCF, 2D and 3D Woven Compositescitations
- 2019Physics of delamination onset in unidirectional composite laminates under mixed-mode I/II loadingcitations
- 2019Damage characterization of adhesively-bonded Bi-material joints using acoustic emissioncitations
- 2010Numerical failure analysis of composite structures
- 2009Study of the mechanical response of carbon Reinforced concrete beams using Non Destructive Techniques during a four-point bending test
- 2009Study of the crack propagation in carbon reinforced concrete beams during a four-point bending test
Places of action
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conferencepaper
Developing health indicators for composite structures based on a two-stage semi-supervised machine learning model using acoustic emission data
Abstract
Composite structures are highly valued for their strength-to-weight ratio, durability, and versatility, making them ideal for a variety of applications, including aerospace, automotive, and infrastructure. However, potential damage scenarios like impact, fatigue, and corrosion can lead to premature failure and pose a threat to safety. This highlights the importance of monitoring composite structures through structural health monitoring (SHM) and prognostics and health management (PHM) to ensure their safe and reliable operation. SHM provides information on the current state of the structure, while PHM predicts its future behavior and determines necessary maintenance. Health indicators (HIs) play a crucial role in both SHM and PHM, providing information on structural health and behavior, but accurate determination of these indicators can be challenging due to the complexity of material behavior and multiple sources of damage in composite structures. In the present work, a model containing a developed adaptive standardization, a dimension reduction sub-model, a time-independent sub-model, and a time-dependent sub-model is introduced to address this challenge. First, the raw data collected by the acoustic emission technique monitoring composite structures under fatigue loading is processed to provide plenty of statistical features. The extracted features are adaptively standardized according to the available data until the current time. Then, the principal component analysis algorithm is employed to reconstruct a few yet highly informative features out of those statistical features. An artificial neural network is used to regress the principal components to the HI that meets the prognostic criteria. Finally, the last sub-model takes into account the time dependency of HI values during fatigue loading. In comparison to other models, the results show superior performance. ; Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project ...