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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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Benedictus, Rinze
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (27/27 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
- 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
- 2022How literature reviews influence the selection of fatigue analysis framework
- 2022Early fatigue damage accumulation of CFRP Cross-Ply laminates considering size and stress level effectscitations
- 2021Fusion-based damage diagnostics for stiffened composite panelscitations
- 2021Modeling and imaging of ultrasonic array inspection of side drilled holes in layered anisotropic mediacitations
- 2020Enhancing the fracture toughness of carbon fibre/epoxy composites by interleaving hybrid meltable/non-meltable thermoplastic veilscitations
- 2020Significantly enhanced structural integrity of adhesively bonded PPS and PEEK composite joints by rapidly UV-irradiating the substratescitations
- 2020The influence of interlayer/epoxy adhesion on the mode-I and mode-II fracture response of carbon fibre/epoxy composites interleaved with thermoplastic veilscitations
- 2020Simulation of ultrasonic beam propagation from phased arrays in anisotropic media using linearly phased multi-Gaussian beamscitations
- 2020A gaussian beam based recursive stiffness matrix model to simulate ultrasonic array signals from multi-layered mediacitations
- 2019Systematic multiparameter design methodology for an ultrasonic health monitoring system for full-scale composite aircraft primary structurescitations
- 2019From thin to extra-thick adhesive layer thicknesses:Fracture of bonded joints under mode I loading conditionscitations
- 2018Experimental assessment of the influence of welding process parameters on Lamb wave transmission across ultrasonically welded thermoplastic composite jointscitations
- 2018Incorporating Inductive Bias into Deep Learning
- 2018Full-scale testing of an ultrasonic guided wave based structural health monitoring system for a thermoplastic composite aircraft primary structure
- 2018The stress ratio effect on plastic dissipation during fatigue crack growthcitations
- 2017Modelling of ultrasonic beam propagation from an array through transversely isotropic fibre reinforced composites using Multi Gaussian beams
- 2017Understanding mixed-mode cyclic fatigue delamination growth in unidirectional compositescitations
- 2016Thermo-viscoelastic analysis of GLAREcitations
- 2016Experimental characterisation of Lamb wave propagation through thermoplastic composite ultrasonic welds
- 2016Effect of fiber-matrix adhesion on the creep behavior of CF/PPS compositescitations
- 2016Experimental investigation of the microscopic damage development at mode i fatigue delamination tips in carbon/epoxy laminatescitations
Places of action
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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>