Date of Award

3-2025

Document Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Department of Electrical and Computer Engineering

First Advisor

Brett J. Borghetti, PhD

Abstract

Determining the extent of manufacturing capabilities with respect to adversarial or hostile nations is a topic of significant importance to the Department of Defense. Manufacturing capabilities can serve as indications of a nation's industrial power and its economy of force in warfare. Remotely detecting machine operations via electromagnetic sensors may be possible via Deep Learning (DL) and Machine Learning (ML) algorithms. To predict machine states, sensor data is collected externally from a machine shop on a college campus to monitor the operating states of lathes and mills in individual and concurrent operation. Furthermore, several sensors are placed in various positions, such as along the powerline connecting to the shop,  to predict machine states based on positional configuration. An initial investigation explores the ability of several ML models to quantify the machine states from a previous experimental implementation. These ML models predict the machine states with accuracies from  95.9% to 98.5%. The highest performing model is then assessed on the most recent experimental implementation alongside a 1D-CNN. The finalized DL models predict each machine's on and off states with accuracies ranging from 80.6% to 93.8%, thereby suggesting the ability to remotely classify machine states.

AFIT Designator

AFIT-ENG-MS-25-M-014

Comments

An embargo was observed for this posting.

Approved for Public Release, Distribution Unlimited. PA case number 88ABW-2025-0209

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