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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
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article
In-flight and wireless damage detection in a UAV composite wing using fiber optic sensors and strain field pattern recognition
Abstract
<p>Aiming to provide more efficient, lightweight structures, composite materials are being extensively used in aerospace vehicles. As the failure mechanisms of these materials are complex, damage detection becomes challenging, requiring advanced techniques for assessing structural integrity and maintaining aircraft safety. In this context, Structural Health Monitoring (SHM) seeks for integrating sensors into the structures in a way that Nondestructive Testing (NDT) is implemented continuously. One promising approach is to use Fiber Optic Sensors (FOS) to acquire strain signals, taking advantages of their capabilities over conventional sensors. Despite several works have developed Health and Usage Monitoring Systems (HUMS) using FOS for performing in-flight SHM in aircraft structures, automatic damage detection using the acquired signals has not been achieved in a robust way against environmental and operational variability, in all flight stages or considering different types of damages. In this work, a HUMS was developed and implemented in an Unmanned Aerial Vehicle (UAV) based on 20 Fiber Bragg Gratings (FBGs) embedded into the composite front spar of the aircraft's wing, a miniaturized data acquisition subsystem for gathering strain signals and a wireless transmission subsystem for remote sensing. The HUMS was tested in 16 flights, six of them were carried out with the pristine structure and the remaining after inducing different artificial damages. The in-flight data were used to validate a previously developed damage detection methodology based on strain field pattern recognition, or strain mapping, which utilizes machine learning algorithms, specifically a Self-Organizing Map (SOM)-based procedure for clustering operational conditions and Principal Component Analysis (PCA) in conjunction with damage indices for final classification. The performance of the damage detection demonstrated a highest accuracy of 0.981 and a highest F<sub>1</sub> score of 0.978. As a main contribution, this work implements in-flight strain monitoring, remote sensing and automatic damage detection in an operating composite aircraft structure.</p>