| 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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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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article
Machine learning at the interface of structural health monitoring and non-destructive evaluation
Abstract
While both non-destructive evaluation (NDE) and structural health monitoring (SHM) share the objective of damage detection and identification in structures, they are distinct in many respects. This paper will discuss the differences and commonalities and consider ultrasonic/guided-wave inspection as a technology at the interface of the two methodologies. It will discuss how data-based/machine learning analysis provides a powerful approach to ultrasonic NDE/SHM in terms of the available algorithms, and more generally, how different techniques can accommodate the very substantial quantities of data that are provided by modern monitoring campaigns. Several machine learning methods will be illustrated using case studies of composite structure monitoring and will consider the challenges of high-dimensional feature data available from sensing technologies like autonomous robotic ultrasonic inspection.