Date of Award

Spring 2025

Document Type

Open Access Thesis

Department

Mechanical Engineering

First Advisor

Austin R.J. Downey

Abstract

Structural Health Monitoring (SHM) is essential for ensuring reliability and longevity of useful structures. Traditional SHM approaches rely on manual or remote data transmission and external processing, which introduce latency and can depend on stable communication links. This work aims to advance SHM by integrating edge-computing techniques for rapid damage detection to enable faster and more autonomous structural assessments in resource-constrained environments. The research spans three key contributions including 1) frequency-based damage detection of civil structures using a sensor package with the addition of an edge processor, 2) additions to the computational efficiency of the edge-computing sensor package in a more resource-constrained environment, and 3) frequency-based damage detection for electronic assemblies with embedded sensors subjected to high-rate dynamic events. The main focus is to enhance structural safety, resilience, and adaptability for structures in critical areas. The first contribution involves drone-deployable vibration sensors and explores the feasibility of edge-computing for autonomous SHM in inaccessible or hazardous environments. The sensors analyze frequency-domain data in real-time to reduce reliance on external data processing. The second contribution constrains the edge-computing abilities of the sensor package to the embedded microcontroller and evaluates the computational efficiency of on-the-edge processing. Lastly, the third contribution demonstrates Fast-Fourier Transform (FFT) analysis for identifying structural damage in printed circuit boards (PCBs) exposed to mechanical shock. This study makes use of embedded sensors to build the foundations of edge-computing for self-diagnosing electronics, also paving the way for active control and damping of vibrations in electronic assemblies. The findings of this work demonstrate the effectiveness of edge-enabled SHM to enhance rapid structural assessments by reducing response times and enabling decision-making at the data source. These advancements contribute to the broader vision of intelligent monitoring systems that can operate autonomously across diverse structural environments.

Rights

© 2025, Ryan Yount

Download

Share

COinS