Abstract
This paper presents a non-destructive evaluation (NDE) technique for detecting damages on a jointed steel plate on the basis of the time of flight and wavelet coefficient, obtained from wavelet transforms of Lamb wave signals. Probabilistic neural networks (PNNs) and support vector machines (SVMs) were applied for pattern classification. In this study, the applicability of the PNNs and SVMs was investigated for the damages in and out of the Lamb wave path. It has been found that the present methods are very efficient in detecting the damages simulated by the loose bolts on the jointed steel plate.
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