Date of Award
Fall 2012
Access Type
Thesis - Open Access
Degree Name
Master of Science in Aerospace Engineering
Department
Aerospace Engineering
Committee Chair
Daewon Kim
First Committee Member
Frank J. Radosta
Second Committee Member
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.
Scholarly Commons Citation
Li, Xiang, "Structural Damage Classification using Support Vector Machines" (2012). Doctoral Dissertations and Master's Theses. 92.
https://commons.erau.edu/edt/92
