Embedded Artificial Intelligence in Guided Wave SHM system: Signal processing, and data analysis

This article presents an overview of recent advances in the development of guided wave (GW) structural health monitoring (SHM) solutions at CEA List. The proposed approach covers the entire data processing chain, from signal acquisition to automated analysis. Both active and passive acquisition techniques are addressed, and the integration of Fibre Bragg Grating (FBG) sensors for operation in challenging environments is explored. Advanced signal processing and imaging methods are embedded directly into acquisition systems, with a strong emphasis on AI-based techniques. First, an efficient AI-driven method for compensating temperature effects is introduced. In addition, the use of simulations to generate large, annotated datasets is presented as a solution to the scarcity of real defect data, enabling the training of deep learning models for automated diagnosis.