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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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Mustapha, F.
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Topics
Publications (9/9 displayed)
- 2016The Effect of Customized Woven and Stacked Layer Orientation on Tensile and Flexural Properties of Woven Kenaf Fibre Reinforced Epoxy Compositescitations
- 2011Optimal Sintering Procedure to Fabrication of Functionally Graded Hydroxyapatite-titaniumcitations
- 2011Fabrication of Functionally Graded Hydroxyapatite-Titanium by Applying Optimal Sintering Procedure and Powder Metallurgy
- 2008Damage localisation in a stiffened composite panel
- 2008Damage localisation in a stiffened composite panelcitations
- 2007Damage location in a stiffened composite panel using lamb waves and neural networks
- 2007Damage detection using stress waves and multivariate statistics, an experimental case study of an aircraft componentcitations
- 2007Damage location in a stiffened composite panel using Lamb waves and neural networks
- 2002A prototype knowledge-based system for material selection of ceramic matrix composites of automotive engine componentscitations
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document
Damage location in a stiffened composite panel using lamb waves and neural networks
Abstract
Neural networks have proved to be very powerful tools in pattern recognition and machine learning and have consequently seen a great deal of applications in Structural Health Monitoring; a field where Pattern Recognition is one of the main lines of attack. The current paper presents a case study of damage detection and location in a stiffened composite panel interrogated by ultrasonic Lamb waves. Rather than work directly on features extracted from the wave profiles, the proposed approach derives secondary features in the form of a vector of novelty indices for the plate. This can be used to train both neural network classifiers and regressors and the use of both for damage location is demonstrated in the paper.