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Fueled by the rapid development of machine learning (ML) and greater accessto cloud computing and graphics processing units (GPUs), various deep learningbased models have been proposed for improving performance of ultrasonic guidedwave structural health monitoring (GW-SHM) systems, especially to countercomplexity and heterogeneity in data due to varying environmental factors(e.g., temperature) and types of damages. Such models typically comprise ofmillions of trainable parameters, and therefore add to cost of deployment dueto requirements of cloud connectivity and processing, thus limiting the scaleof deployment of GW-SHM. In this work, we propose an alternative solution thatleverages TinyML framework for development of light-weight ML models that couldbe directly deployed on embedded edge devices. The utility of our solution isillustrated by presenting an unsupervised learning framework for damagedetection in honeycomb composite sandwich structure (HCSS) with disbond anddelamination type of damages, validated using data generated by finite element(FE) simulations and experiments performed at various temperatures in the range0{\deg}C to 90{\deg}C. We demonstrate a fully-integrated solution using aXilinx Artix-7 FPGA for data acquisition and control, and edge-inference ofdamage.

Unsupervised deep learning framework for temperature-compensated damage assessment using ultrasonic guided waves on edge device