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

Significance Statement

Carbon-fibre-reinforced-plastic (CFRP) composite materials are widely employed in mechanical and aerospace infrastructures due to their high strength to weight ratio. However, CFRP materials are susceptible to damage induced by low-velocity impacts. The detection of a barely visible damage condition is hence a fundamental requirement of any health monitoring routine. The proposed methodology is based on the autoregressive (AR) models obtained from the vibrating time-domain responses. Two piezo-patches only were employed for actuation and sensing purposes. The detection and the classification of CFRP composite laminates subjected to different low-velocity impacts are achieved by means of statistical pattern recognition of the extracted autoregressive models-based features. 

Detection of low-velocity impact-induced delaminations in composite laminates using AutoRegressive models. Advances in Engineering

About the author

Davide Nardi received his MSc in aeronautical engineering from Sapienza University (Rome). He is currently pursuing his final PhD year at the Department of Mechanical and Aerospace Engineering at Sapienza University under the supervision of prof. Paolo Gaudenzi. His research has been mainly focused on vibration-based structural health monitoring of composite plates via statistical pattern recognition of proper damage sensitive features.  

Journal Reference

Composite Structures, February 2016.

Davide Nardi , Luca Lampani, Michele Pasquali, Paolo Gaudenzi

University of Rome La Sapienza, Via Eudossiana 18, Rome 00184, Italy


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 autoregressive (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 autoregressive 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 autoregressive 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.


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