| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
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
| Organizations | Location | People |
|---|
article
Intelligent health indicator construction for prognostics of composite structures utilizing a semi-supervised deep neural network and SHM data
Abstract
<p>A health indicator (HI) is a valuable index demonstrating the health level of an engineering system or structure, which is a direct intermediate connection between raw signals collected by structural health monitoring (SHM) methods and prognostic models for remaining useful life estimation. An appropriate HI should conform to prognostic criteria, i.e., monotonicity, trendability, and prognosability, that are commonly utilized to measure the HI's quality. However, constructing such a HI is challenging, particularly for composite structures due to their vulnerability to complex damage scenarios. Data-driven models and deep learning are powerful mathematical tools that can be employed to achieve this purpose. Yet the availability of a large dataset with labels plays a crucial role in these fields, and the data collected by SHM methods can only be labeled after the structure fails. In this respect, semi-supervised learning can incorporate unlabeled data monitored from structures that have not yet failed. In the present work, a semi-supervised deep neural network is proposed to construct HI by SHM data fusion. For the first time, the prognostic criteria are used as targets of the network rather than employing them only as a measurement tool of HI's quality. In this regard, the acoustic emission method was used to monitor composite panels during fatigue loading, and extracted features were used to construct an intelligent HI. Finally, the proposed roadmap is evaluated by the holdout method, which shows a 77.3% improvement in the HI's quality, and the leave-one-out cross-validation method, which indicates the generalized model has at least an 81.77% score on the prognostic criteria. This study demonstrates that even when the true HI labels are unknown but the qualified HI pattern (according to the prognostic criteria) can be recognized, a model can still be built that provides HIs aligning with the desired degradation behavior.</p>