In this paper, the detection of delaminations in carbon-fiber-reinforced-plastic (CFRP) laminate plates induced by low-velocity impacts (LVI) is investigated by means of Auto-Regressive (AR) models obtained from the time histories of the acquired responses of the composite specimens. A couple of piezoelectric patches for actuation and sensing purposes are employed. The proposed structural health monitoring (SHM) routine begins with the selection of the suitable locations of the piezoelectric transducers via the numerical analysis of the curvature mode shapes of the CFRP plates. The normalized data recorded for the undamaged plate configuration are then analyzed to obtain the most suitable AR model using five techniques based on the Akaike Information Criterion (AIC), the Akaike Final Prediction Error (FPE), the Partial Autocorrelation Function (PAF), the Root Mean Squared (RMS) of the AR residuals for different order p, and the Singular Value Decomposition (SVD). Linear Discriminant Analysis (LDA) is then applied on the AR model parameters to enhance the performance of the proposed delamination identification routine. Results show the effectiveness of the developed procedure when a reduced number of sensors is available.

Detection of low-velocity impact-induced delaminations in composite laminates using Auto-Regressive models / Nardi, Davide; Lampani, Luca; Pasquali, Michele; Gaudenzi, Paolo. - In: COMPOSITE STRUCTURES. - ISSN 0263-8223. - 151:(2016), pp. 108-113. [10.1016/j.compstruct.2016.02.005]

Detection of low-velocity impact-induced delaminations in composite laminates using Auto-Regressive models

NARDI, DAVIDE
;
LAMPANI, LUCA;PASQUALI, MICHELE;GAUDENZI, Paolo
2016

Abstract

In this paper, the detection of delaminations in carbon-fiber-reinforced-plastic (CFRP) laminate plates induced by low-velocity impacts (LVI) is investigated by means of Auto-Regressive (AR) models obtained from the time histories of the acquired responses of the composite specimens. A couple of piezoelectric patches for actuation and sensing purposes are employed. The proposed structural health monitoring (SHM) routine begins with the selection of the suitable locations of the piezoelectric transducers via the numerical analysis of the curvature mode shapes of the CFRP plates. The normalized data recorded for the undamaged plate configuration are then analyzed to obtain the most suitable AR model using five techniques based on the Akaike Information Criterion (AIC), the Akaike Final Prediction Error (FPE), the Partial Autocorrelation Function (PAF), the Root Mean Squared (RMS) of the AR residuals for different order p, and the Singular Value Decomposition (SVD). Linear Discriminant Analysis (LDA) is then applied on the AR model parameters to enhance the performance of the proposed delamination identification routine. Results show the effectiveness of the developed procedure when a reduced number of sensors is available.
2016
Auto-Regressive model; Carbon-fiber-reinforced-plastic; Delamination; Low-velocity impact; Structural health monitoring; Civil and Structural Engineering; Ceramics and Composites
01 Pubblicazione su rivista::01a Articolo in rivista
Detection of low-velocity impact-induced delaminations in composite laminates using Auto-Regressive models / Nardi, Davide; Lampani, Luca; Pasquali, Michele; Gaudenzi, Paolo. - In: COMPOSITE STRUCTURES. - ISSN 0263-8223. - 151:(2016), pp. 108-113. [10.1016/j.compstruct.2016.02.005]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/868512
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