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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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Sierra, Julian
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (11/11 displayed)
- 2021Toward Structural Health Monitoring of Civil Structures Based on Self-Sensing Concrete Nanocompositescitations
- 2020In-flight and wireless damage detection in a UAV composite wing using fiber optic sensors and strain field pattern recognitioncitations
- 2019Artificial Intelligence Metamodeling Approach to Design Smart Composite Laminates with Bend-Twist Couplingcitations
- 2019Synthesis and characterization of cement/carbon-nanotube composite for structural health monitoring applicationscitations
- 2019Structural design and manufacturing process of a low scale bio-inspired wind turbine bladescitations
- 2018Structural health monitoring using carbon nanotube/epoxy composites and strain-field pattern recognition
- 2018Damage detection in composite aerostructures from strain and telemetry data fusion by means of pattern recognition techniques
- 2017Structural health monitoring on an unmanned aerial vehicle wing's beam based on fiber Bragg gratings and pattern recognition techniquescitations
- 2016Damage and nonlinearities detection in wind turbine blades based on strain field pattern recognition. FBGs, OBR and strain gauges comparisoncitations
- 2014A robust procedure for damage identification in a lattice spacecraft structural element by mean of Strain field pattern recognition techniques
- 2014Strain measurements and damage detection in large composite structures by fiber optics sensors
Places of action
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article
Damage and nonlinearities detection in wind turbine blades based on strain field pattern recognition. FBGs, OBR and strain gauges comparison
Abstract
<p>A 13.5 m wind turbine blade prototype was designed and made of glass fiber and vinylester resin doped with carbon nanofibers. The blade was manufactured using Light RTM (Resin Transfer Molding) as a monocoque structure with a PVC foam core.Within the presented work, a methodology for instrumenting the blade with Fiber Optic Sensors (FOS) embedded into the structure during the manufacturing process was developed. Two different FOS technologies for strain sensing were embedded into the blade: Fiber Bragg Gratings (FBGs) and a plain fiber optic for distributed sensing using an Optical Backscatter Reflectometer (OBR). Besides the FOS, traditional electrical extensometers were bonded to the surface of the blade.By using Hierarchical nonlinear principal component analysis (h-NLPCA) it was possible to perform a pattern recognition technique based on the strain field inferred from the measurements acquired by different sensors. Defects and nonlinearities could be detected during the certification testing of the blades, avoiding the premature failure of the structure.Several static tests were conducted, including a test campaign with known artificial damages induced into the structure and the sensitivity of the technique was evaluated. The results showed that every damages could be detected by using different sensing techniques.</p>