Ultrasonic guided waves are recognized as one of the promising approaches to detect damage in the structures. Furthermore, machine learning algorithms with their distinguished capability in recognizing and classifying the patterns in a dataset can be employed as an extension to conventional damage detection techniques. In the current study, a damage detection framework is proposed, which is based on the result of the numerical simulation of wave propagation in an aluminum plate. The feature vector required for the training of the classifiers is generated by employing statistical analysis of the time domain signals as well as their Hilbert transform. This study shows the importance of the selection of the right combination of the features for the classification of the state of the structure. The result of this study contributes to devising a more automated and generalized process for damages detection in metallic structures.

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