For many engineering and aerospace applications, detection and quantification of multiscale damage in fiberreinforced composite structures is increasing in importance. Consequently, the development of an efficient and costeffective diagnosis scheme that can accurately sense, characterize, and evaluate the existence of any form of damage will offer significant potential for improving the performance, reliability, and extending the operational life of these complex systems. We present an approach to characterize and classify different damage states in composite laminates by measuring the change in the signature of the resultant wave that propagates through the anisotropic media under forced excitation. The wave propagation is measured using surface-mounted piezoelectric transducers. Sensor signals collected from test specimens with various forms of induced damage are analyzed using a patternrecognition algorithm known as the one-class support vector machines. The one-class support-vector-machine algorithm performs automatic anomaly detection and classification of damage signatures using various features from the sensor readings. The results obtained suggest that the one-class support-vector-machine algorithm, along with appropriate preprocessing techniques, can often achieve better accuracy than the popular k-nearest-neighbor method in detecting and classifying anomalies caused by structural defects, even when the perturbations caused in the sensor signals due to different damage states are minimal. Nomenclature f x = decision function K x; xj = inner product kernel Rij = matrix with correct classification rate w = weight vector w n = random noise xi = input pattern yi = label i = Lagrangian multipliers = slack variable = bias = time delay = kernel width I
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