Deep LSTM-based autoencoder for CFRP damage imaging using Lamb wave and correlation factor weighted delay-and-sum algorithm

Abstract

The ultrasonic Lamb wave testing (ULWT) has proven valuable in non-destructive testing (NDT) due to its high sensitivity and wide coverage. However, the classical post-processing algorithm, the delay-and-sum (DAS) technique, suffers from notable artifacts and inadequate accuracy in Lamb wave inspection due to the existence of reflection and superposition, especially on composite materials. In this study, we proposed a novel algorithm, the correlation factor weighted DAS imaging algorithm based on the Long short-term memory-autoencoder (LSTM-AE), to address these deterioration issues. The LSTM-AE demonstrates the capability to extract potential features and accurately reconstruct input signals. By incorporating the concept of anomaly detection, an LSTM-AE trained with intact signals produces a significantly distorted output when exposed to damaged signals as input. We formulated a novel damage index (DI) based on the Pearson Correlation Coefficient (PCC) between the output and input signals as the weighting factor. The proposed method has undergone experimental validation, confirming its effectiveness in Lamb wave inspection.