Investigation of Feature Inputs for Binary Classification of Ultrasonic NDT Signals using SVM and Neural Networks

Abstract

Classification of Ultrasonic Non-Destructive Testing (NDT) signals can be done by Machine Learning models including Support Vector Machine (SVM) and Neural Networks (NN). The main objective of this study is to classify the ultrasonic A-scan data either as flaw echoes or clutter echoes (no flaw). The signal pre-processing has been done using Discrete Wavelet Transform (DWT). The refined low pass output has been used as feature input to the machine learning algorithms either directly or as a power signal. In case of SVM, direct low pass output in windowed format was tested with linear kernel and Radial Basis Function (RBF) kernel and the power signal of the low pass DWT in the windowed format was also tested with linear kernel and RBF kernel. SVM simulation results show that the direct low pass signal with linear kernel fails to converge while power of the low pass DWT achieves an accuracy of around 95%. RBF kernel accuracy was around 98% irrespective of the format of the signal. In case of Neural Network, both the direct low pass output and the power of the low pass output were tested and it was found that the direct low pass output with NN yielded 94% accuracy while the power of the low pass output with NN yielded an accuracy of 98%.