Damage Quantification in Composite Structures Using Autoregressive Models

When small damage is detected in its initial stage in a real structure, it is necessary to decide if the user must repair immediately or keep on safely monitoring it. Regarding the second choice, the present paper proposes a methodology for damage severity quantification of delamination extension in composite structures based on a data-driven strategy using autoregressive modeling approach for Lamb wave propagation. A pair of features is used based on the autoregressive (AR) model coefficients and residuals and a machine learning algorithm with Mahalanobis Squared Distance for outlier detection. The damage severity quantification is proposed through an experimentally identified smoothing spline trend curve between the damage index and its severity. The application of the methodology is demonstrated in a composite plate with various progressive damage scenarios. The proposed method proved to be able to identify and predict the localization and the damage index related to its respective extension of minimal simulated damage with promising accuracy.

[1]  Charles R. Farrar,et al.  Nonlinear System Identification for Damage Detection , 2007 .

[2]  M. Rébillat,et al.  Generation of controlled delaminations in composites using symmetrical laser shock configuration , 2017 .

[3]  Genshiro Kitagawa.  Introduction to Time Series Modeling , 2010 .

[4]  Vicente Lopes Junior,et al.  Structural Health Monitoring in Smart Structures Through Time Series Analysis , 2008 .

[5]  A. Bowman,et al.  Applied smoothing techniques for data analysis : the kernel approach with S-plus illustrations , 1999 .

[6]  Carl M. O’Brien.  Smoothing Splines: Methods and Applications by Yuedong Wang , 2012 .

[7]  Damage detection on composite beam under transverse impact using the Wave Finite Element method , 2019, Applied Acoustics.

[8]  Charles R. Farrar,et al.  Influence of the Autoregressive Model Order on Damage Detection , 2011, Comput. Aided Civ. Infrastructure Eng..

[9]  Marc Rébillat,et al.  Automatic damage type classification and severity quantification using signal based and nonlinear model based damage sensitive features , 2019, Journal of Process Control.

[10]  A. S. Nobari,et al.  Vibration-Based Techniques for Damage Detection and Localization in Engineering Structures , 2018 .

[11]  Charles R. Farrar,et al.  Use of Relative Baseline Features of Guided Waves for In situ Structural Health Monitoring , 2011 .

[12]  Samuel da Silva,et al.  On the application of discrete-time Volterra series for the damage detection problem in initially nonlinear systems , 2017 .

[13]  Z. Su,et al.  Identification of Damage Using Lamb Waves , 2009 .

[14]  Charles R. Farrar,et al.  Structural health monitoring algorithm comparisons using standard data sets , 2009 .

[15]  Marc Rébillat,et al.  Damage localization in composite plates using canonical polyadic decomposition of Lamb wave difference signals tensor , 2018 .

[16]  V. Memmolo,et al.  Experimental characterization of a damage detection and localization system for composite structures , 2018, Measurement.

[17]  Paolo Gaudenzi,et al.  Detection of low-velocity impact-induced delaminations in composite laminates using Auto-Regressive models , 2016 .

[18]  Diego Alexander Tibaduiza Burgos,et al.  Structural damage detection and classification based on machine learning algorithms , 2016 .