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On the Processing of Leaky Guided Ultrasonic Waves for the Noncontact Inspection of Underwater Structures

In this paper, we present the results of an experimental study where leaky guided ultrasonic waves were used for the noncontact health monitoring of an aluminum plate immersed in water. Leaky guided waves were generated by means of a pulsed laser and detected by five immersion transducers arranged in a semi-circular array. The signals were processed to extract eight damage-sensitive features from the time, frequency, and joint time-frequency domains. These features were then fed to a supervised learning algorithm based on artificial neural networks to classify five types of damages artificially devised on the plate. We found that the hybrid laser-immersion transducers system and signal processing technique enable the detection of the defects, and the neural network provides higher success rate.

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