Health Monitoring of Aerospace Structures Using Fibre Bragg Gratings Combined with Advanced Signal Processing and Pattern Recognition Techniques

:  The main purpose of this work is to develop an innovative system for structural health monitoring of aerospace composite structures based on real‐time dynamic strain measurements. The dynamic response of a composite panel, which represents a section of a typical aeronautical structure, is measure...

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Bibliographic Details
Published in:Strain 2012-06, Vol.48 (3), p.267-277
Main Authors: Panopoulou, A., Roulias, D., Loutas, T. H., Kostopoulos, V.
Format: Article
Language:English
Subjects:
Algorithms
Computer simulation
Damage
dynamic strain measurements
Dynamical systems
Dynamics
fibre Bragg grating sensors (FBG)
Health monitoring (engineering)
Mathematical models
neural network
Panels
Position (location)
structural health monitoring (SHM)
vibration testing
wavelet analysis
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Summary::  The main purpose of this work is to develop an innovative system for structural health monitoring of aerospace composite structures based on real‐time dynamic strain measurements. The dynamic response of a composite panel, which represents a section of a typical aeronautical structure, is measured using fibre Bragg grating (FBG) dynamic sensors. Damage is simulated by slightly varying locally the mass of the panel at different zones of the structure. A finite element model of the structure has been developed to simulate the dynamic behaviour based on the modal superposition principle. The numerical model was calibrated against experimental results, and it was used for the placement of the FBG sensors. The proposed damage detection algorithm utilises the collected dynamic response data, and through various levels of data processing, an artificial neural network identifies the damage size and location. Feature extraction is the first step of the algorithm. Novel digital signal processing techniques, such as the wavelet transform, are used for feature extraction. The extracted features are effective indices of damage location and its extension. The classification step comprises a feed‐forward back propagation network, whose output determines the simulated damage location. Finally, dedicated training and validation activities are carried out by means of numerical simulations and experimental procedures.
ISSN:0039-2103
1475-1305
DOI:10.1111/j.1475-1305.2011.00820.x