Date of Award

Fall 2012

Document Type

Thesis - Open Access

Degree Name

Master of Science in Aerospace Engineering

Department

Aerospace Engineering

Committee Chair

Dr. Daewon Kim

First Committee Member

Dr. Frank J. Radosta

Second Committee Member

Dr. Yi Zhao

Abstract

In this research, a methodology to classify crack and corrosion metallic damages using a time-frequency representation method and support vector machines is investigated. Piezoelectric ceramic actuators are utilized to generate guided wave signals on a set of aluminum beam coupons with different damage features, such as types, locations, and thicknesses. The short-time Fourier transform is applied to analyze the measured signals. For damage classification, the spectrograms obtained from finite element models are employed to train a two-class support vector machine learning classifier. The classifier is able to correctly classify different types of damages based upon the measured signals collected from the unknown damage sources. A multiple-class classifier is also generated to predict the damage extent of crack samples.

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