Delamination detection in composite laminates using auto-regressive models of vibration signals

Abstract eng:
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 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 through the analysis of the numerical curvature mode shapes of the CFRP plates. Then the normalized data recorded of the pristine plate configuration are analyzed in order to obtain the most suitable AR model through five techniques based on Akaike Information Criterion (AIC), Akaike Final Prediction Error (FPE), Partial Autocorrelation Function (PAF), Root Mean Squared (RMS) of the AR residuals for different order p, and Singular Value Decomposition (SVD). Linear Discriminant Analysis (LDA) is then applied on the AR models parameters, i.e. the damage sensitive features extracted, 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 required.

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