Weld defect classification in ultrasonic testing basing on timefrequency discriminant features

According to transient property of ultrasonic signal, the discriminant pursuit method was proposed to extract local time-frequency features of defect signal and the features were fed to a probabilistic neural networks to classify the defects.During extracting features, the correlation between the incoming atom and the atoms selected before was considered to reduce the redundance among the selected atoms so that the extracted features discriminated different class of signals effectively.Finally, the defects of an electronic welded joint were classified by proposed approach, and the experimental results show that time-frequency discriminant features are appropriate for defects classification in ultrasonic testing, and can suppress the effect of grain noise.In addition, the higher accuracy can be reached if considering the correlation of the selected atoms.