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| Naji, M. |
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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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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Kočí, Jan | Prague |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Azevedo, Nuno Monteiro |
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| Rignanese, Gian-Marco |
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Worden, K.
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (33/33 displayed)
- 2020Machine learning at the interface of structural health monitoring and non-destructive evaluationcitations
- 2020Machine learning at the interface of structural health monitoring and non-destructive evaluationcitations
- 2019On the performance of a cointegration-based approach for novelty detection in realistic fatigue crack growth scenarioscitations
- 2018Acoustic emission source characterisation using evolutionary optimisation
- 2016Novelty detection and dimension reduction via guided ultrasonic waves:Damage monitoring of scarf repairs in composite laminatescitations
- 2016Novelty detection and dimension reduction via guided ultrasonic waves: Damage monitoring of scarf repairs in composite laminatescitations
- 2015Continuous debonding monitoring of a patch repaired helicopter stabilizer:Damage assessment and analysiscitations
- 2015Damage monitoring of external patch repairs with guided ultrasonic wavescitations
- 2015Continuous debonding monitoring of a patch repaired helicopter stabilizer: Damage assessment and analysiscitations
- 2014Bayesian sensitivity analysis of flight parameters that affect main landing gear yield locations
- 2013On the structural health monitoring of repaired aerospace structures
- 2013Structural health monitoring and damage prognosis in composite repaired structures through the excitation of guided ultrasonic wavescitations
- 2011On impact damage detection and quantification for CFRP laminates using structural response data only
- 2011On impact damage detection and quantification for CFRP laminates using structural response data only
- 2011A cellular automaton model for predicting intergranular corrosion
- 2011Some experimental observations on the detection of composite damage using lamb wavescitations
- 2011On impact damage detection and quantification for CFRP laminatescitations
- 2011Principal component analysis of acoustic emission signals from landing gear componentscitations
- 2009Strategies for using cellular automata to locate constrained layer damping on vibrating structurescitations
- 2009Impact damage detection and quantification in CFRP laminates; a precursor to machine learning
- 2009Identification of impact damage in CRRP laminates using the NDT approach
- 2009Identification of impact damage in CRRP laminates using the NDT approach
- 2009Identification of impact damage in CRRP laminates using the NDT approach
- 2008Damage localisation in a stiffened composite panel
- 2008Damage localisation in a stiffened composite panelcitations
- 2008The effects of uncertainties within acoustic emission modelling
- 2008A cellular automaton based model for predicting intergranular corrosion in aerospace alloys
- 2008Model-based prognosis for intergranular corrosion
- 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
- 2006On the reproducibility of transducer coupling for acoustic emission testing
- 2001On the long-term stability of normal condition for damage detection in a composite panel
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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 <br/>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.