Theses and Dissertations

ORCID

https://orcid.org/0000-0001-8112-1899

Issuing Body

Mississippi State University

Advisor

Moorhead, Robert J.

Committee Member

Ball, John E.

Committee Member

Kurum, Mehmet

Committee Member

Gurbuz, Ali

Date of Degree

5-12-2023

Document Type

Dissertation - Open Access

Major

Electrical and Computer Engineering

Degree Name

Doctor of Philosophy (Ph.D)

College

James Worth Bagley College of Engineering

Department

Department of Electrical and Computer Engineering

Abstract

This dissertation is a study of methods to automatedly detect and produce approximations of eddy current differential coil defect signatures in terms of a summed collection of Gaussian functions (SoG). Datasets consisting of varying material, defect size, inspection frequency, and coil diameter were investigated. Dimensionally reduced representations of the defect responses were obtained utilizing common existing reduction methods and novel enhancements to them utilizing SoG Representations. Efficacy of the SoG enhanced representations were studied utilizing common Machine Learning (ML) interpretable classifier designs with the SoG representations indicating significant improvement of common analysis metrics.

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