XAI for signal analysis of guided wave testing at pipe elbows

Abstract

Guided wave testing can provide an efficient screening method for local wall thinning of piping owing to its long inspection range and its ability to inspect pipes with limited access. However, there is a problem in signal interpretation of guided wave testing. While the interpretation of echo signals in guided wave testing is relatively simple for straight pipes, it becomes much more difficult when inspected piping includes an elbow because wave propagation becomes more complicated. This study investigates a signal analysis method that utilizes deep learning to evaluate local wall-thinning at an elbow based on echo signals in guided wave testing using multiple frequencies. To this end, we considered the configuration of deep learning system to analyze echo signals in guided wave testing and utilized Shapley additive explanations (SHAP), one of the explainable AI (XAI) techniques, to provide reasoning for prediction results obtained by deep learning models.