20 April 2016 Regularized discriminant analysis for multi-sensor decision fusion and damage detection with Lamb waves
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Abstract
In this study we propose a regularized linear discriminant analysis approach for damage detection which does not require an intermediate feature extraction step and therefore more efficient in handling data with high-dimensionality. A robust discriminant model is obtained by shrinking of the covariance matrix to a diagonal matrix and thresholding redundant predictors without hurting the predictive power of the model. The shrinking and threshold parameters of the discriminant function (decision boundary) are estimated to minimize the classification error. Furthermore, it is shown how the damage classification achieved by the proposed method can be extended to multiple sensors by following a Bayesian decision-fusion formulation. The detection probability of each sensor is used as a prior condition to estimate the posterior detection probability of the entire network and the posterior detection probability is used as a quantitative basis to make the final decision about the damage.
Conference Presentation
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Spandan Mishra, Spandan Mishra, O. Arda Vanli, O. Arda Vanli, Fred W. Huffer, Fred W. Huffer, Sungmoon Jung, Sungmoon Jung, "Regularized discriminant analysis for multi-sensor decision fusion and damage detection with Lamb waves", Proc. SPIE 9803, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2016, 98032H (20 April 2016); doi: 10.1117/12.2217959; https://doi.org/10.1117/12.2217959
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