Automatic Classification of Weld Defects From Ultrasonic Signals Using WPEE-KPCA Feature Extraction and an ABC-SVM Approach

Abstract

The classification of weld defects is very important for the safety assessment of welded structures and feature extraction of ultrasonic defect signals is vital for defect classification. A novel approach based on wavelet packet energy entropy (WPEE) and kernel principal component analysis (KPCA) feature extraction and an artificial bee colony optimisation support vector machine (ABC-SVM) classifier is proposed in this paper. Firstly, the WPEE method is adopted to extract ultrasonic signal features of weld defects and KPCA is used for feature selection. Secondly, an ABC-SVM classifier is employed to perform defect classification. Finally, experiments involving defect feature extraction, selection and classification are carried out using four types of weld defect. The results demonstrate that the performance of the proposed feature extraction method based on WPEE is superior to that of wavelet packet energy (WPE). In addition, the WPEE-KPCA method achieved a higher accuracy rate of defect classification than WPEE.