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

TL;DR

This paper introduces an unsupervised deep learning framework using TinyML for temperature-compensated damage detection in structures via ultrasonic guided waves, enabling deployment on low-power edge devices.

Contribution

It presents a novel lightweight, unsupervised ML model for damage assessment that operates on embedded edge hardware, addressing deployment challenges of traditional deep learning models.

Findings

Effective damage detection across temperature variations

Successful deployment on FPGA-based edge device

Validated with simulations and experimental data

Abstract

Fueled by the rapid development of machine learning (ML) and greater access to cloud computing and graphics processing units (GPUs), various deep learning based models have been proposed for improving performance of ultrasonic guided wave structural health monitoring (GW-SHM) systems, especially to counter complexity and heterogeneity in data due to varying environmental factors (e.g., temperature) and types of damages. Such models typically comprise of millions of trainable parameters, and therefore add to cost of deployment due to requirements of cloud connectivity and processing, thus limiting the scale of deployment of GW-SHM. In this work, we propose an alternative solution that leverages TinyML framework for development of light-weight ML models that could be directly deployed on embedded edge devices. The utility of our solution is illustrated by presenting an unsupervised learning framework for damage detection in honeycomb composite sandwich structure (HCSS) with disbond and delamination type of damages, validated using data generated by finite element (FE) simulations and experiments performed at various temperatures in the range 0{\deg}C to 90{\deg}C. We demonstrate a fully-integrated solution using a Xilinx Artix-7 FPGA for data acquisition and control, and edge-inference of damage.