A New Classifier Network for Ultrasonic NDE Applications based on Ensemble Deep Learning

This work presents a classifier architecture for Non-Destructive Evaluation (NDE) applications which can robustly detect the presence and location of flaws using an ensemble of deep learning networks. The ensemble draws upon the effective sequential time analysis of Long Short-Term Memory - Neural Networks (LSTM-NN), the function estimation and prediction properties of Wavelet Neural Networks (WNN), and the feature extraction capabilities of Convolution Neural Networks (CNN). Simulation results confirm that the proposed architecture offers highly reliable flaw detection and localization with significant Flaw to Clutter Ratio (FCR) enhancements.