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.@en

The detection of delaminations in composite structures due to impacts is a critical issue for any structural health monitoring (SHM) programme. In this work, an experimental campaign is carried out to investigate the presence of delamination induced in carbon-fibre-reinforced-plastic (CFRP) plates by low velocity impacts (LVI). A reduced number of piezoelectric devices for actuation and sensing purposes are employed. The proposed study starts with the choice of the proper positions of the piezoelectric sensor and actuator through the analysis of the numerical curvature mode shapes of the composite laminated plate. A wavelet packet transform (WPT)-based algorithm is applied on the vibrating response of the plates to extract wavelet-based damage sensitive features. Linear Discriminant Analysis (LDA) is then applied on the extracted damage sensitive features to enhance the performance of the proposed delamination identification procedure. Results show the effectiveness of the developed SHM routine when a reduced number of sensors is either desirable or mandatory.@en