| 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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Moradi, Morteza
Delft University of Technology
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (11/11 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
- 2023Acousto-ultrasonic composite transducers integration into thermoplastic composite structures via ultrasonic welding
- 2023Intelligent health indicator construction for prognostics of composite structures utilizing a semi-supervised deep neural network and SHM datacitations
- 2023Developing health indicators for composite structures based on a two-stage semi-supervised machine learning model using acoustic emission datacitations
- 2023Delamination Size Prediction for Compressive Fatigue Loaded Composite Structures Via Ultrasonic Guided Wave Based Structural Health Monitoring
- 2021Effect of honeycomb core on free vibration analysis of fiber metal laminate (FML) beams compared to conventional compositescitations
- 2020Investigation of nonlinear post-buckling delamination in curved laminated composite panels via cohesive zone modelcitations
- 2019Edge disbond detection of carbon/epoxy repair patch on aluminum using thermographycitations
- 2019Numerical and experimental study for assessing stress in carbon epoxy composites using thermographycitations
- 2019Post buckling behavior analysis of unidirectional saddle shaped composite panels containing delaminations using cohesive zone modeling
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 ...