Wavelet transform-based feature extraction for ultrasonic flaw signal classification

In this paper, we present automatic classification models for ultrasonic flaw signals acquired from carbon-fiber-reinforced polymer specimens. Different state-of-the-art strategies based on wavelet transform are utilized for feature extraction. Furthermore, a wavelet packet transform-based local energy feature extraction method is proposed to solve the deficiencies of the existing methods. Artificial neural networks and support vector machines are trained to validate the effectiveness of different feature extraction methods for flaw signal classification. Experimental results show that the proposed method can extract reliable features to effectively classify the different ultrasonic flaw signals with high accuracy.