Abstract
Abstract—In this paper, we present a automatic system to classify the ultrasonic flaw signals acquired from carbon fiber reinforced polymer (CFRP) specimens with void, delamination and debonding. Different strategies based on discrete wavelet transform (DWT) and wavelet packet transform (WPT) are utilized for feature extraction. After that, the linear mapping has been applied from wavelet domain to principal components analysis (PCA) domain for dimension reduction. Artificial neural networks (ANNs) and support vector machines (SVMs) are trained to validate the effectiveness of different wavelet transform based features for flaw signal classification. Experimental results show that the normalized energy of WPT coefficients and statistical parameters of WPT representation of original signal can construct the reliable features to effectively classify the different ultrasonic flaw signals with high accuracy and low training elapsed time.
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