Reliability assessment of guided wave damage localization with deep learning uncertainty quantification methods

Abstract

Guided wave-based structural health monitoring is an attractive option for detecting structural defects in an automated manner. In this work, we focus on the task of damage localization. Deep learning methods have been shown to have superior performance for damage localization. Yet, environmental variations introduce uncertainty in the system and reduce its reliability. For this reason, it is crucial to assess the reliability of estimates taken from structural health monitoring systems. In this work, we estimate the localization reliability from a single snapshot of sparse array guided wave measurements instead of reporting values averaged over an entire set of test measurements. The assessment strategy can be added to any deep learning localization model and produces both a localization and uncertainty estimate. The deep learning model is trained using only guided wave simulations. We assess the uncertainty using both simulated and experimental data with temperature variations. Multiple deep learning-based uncertainty quantification methods are analyzed. Results demonstrate correlations between uncertainty, temperature variations, and the presence of synthetic damage. We also compare with reliability derived from delay-and-sum localization. We find that a deep ensemble learning strategy provides the most reliable damage localization and uncertainty quantification.