A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Smart monitoring of aeronautical composites plates based on electromechanical impedance measurements and artificial neural networks
Highlights Electromechanical impedance is used for damage localization in composites. A coupled FEM approach (Abaqus/Matlab) is used for updating the baseline model. The diagnosis is made by local expert (PNN) depending on type/class of damage. Probabilistic Neural Networks (PNNs) are well adapted to multiscale localization. Damage due to small energy of impact needs larger database to be recognize.
Abstract This paper presents a structural health monitoring (SHM) method for in situ damage detection and localization in carbon fiber reinforced plates (CFRPs). The detection is achieved using the electromechanical impedance (EMI) technique employing piezoelectric transducers as high-frequency modal sensors. Numerical simulations based on the finite element method are carried out so as to simulate more than a hundred damage scenarios. Damage metrics are then used to quantify and detect changes between the electromechanical impedance spectrum of a pristine and damaged structure. The localization process relies on artificial neural networks (ANNs) whose inputs are derived from a principal component analysis of the damage metrics. It is shown that the resulting ANN can be used as a tool to predict the in-plane position of a single damage in a laminated composite plate.
Smart monitoring of aeronautical composites plates based on electromechanical impedance measurements and artificial neural networks
Highlights Electromechanical impedance is used for damage localization in composites. A coupled FEM approach (Abaqus/Matlab) is used for updating the baseline model. The diagnosis is made by local expert (PNN) depending on type/class of damage. Probabilistic Neural Networks (PNNs) are well adapted to multiscale localization. Damage due to small energy of impact needs larger database to be recognize.
Abstract This paper presents a structural health monitoring (SHM) method for in situ damage detection and localization in carbon fiber reinforced plates (CFRPs). The detection is achieved using the electromechanical impedance (EMI) technique employing piezoelectric transducers as high-frequency modal sensors. Numerical simulations based on the finite element method are carried out so as to simulate more than a hundred damage scenarios. Damage metrics are then used to quantify and detect changes between the electromechanical impedance spectrum of a pristine and damaged structure. The localization process relies on artificial neural networks (ANNs) whose inputs are derived from a principal component analysis of the damage metrics. It is shown that the resulting ANN can be used as a tool to predict the in-plane position of a single damage in a laminated composite plate.
Smart monitoring of aeronautical composites plates based on electromechanical impedance measurements and artificial neural networks
Selva, Pierre (author) / Cherrier, Olivier (author) / Budinger, Valérie (author) / Lachaud, Frédéric (author) / Morlier, Joseph (author)
Engineering Structures ; 56 ; 794-804
2013-05-13
11 pages
Article (Journal)
Electronic Resource
English
Monitoring of laminated composites delamination based on electromechanical impedance Measurement
| British Library Online Contents | 2004
Quantitative Corrosion Monitoring Using Wireless Electromechanical Impedance Measurements
| British Library Online Contents | 2010
Impedance-Based Structural Health Monitoring with Artificial Neural Networks
| British Library Online Contents | 2000
Impedance-based structural health monitoring with artificial neural networks
| Tema Archive | 2000