Structural Damage Classification using Support Vector Machines

<p>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.</p>

Name thesis:degree_name

Master of Science in Aerospace Engineering

Level thesis:degree_level

Thesis - Open Access

Discipline thesis:degree_discipline

Aerospace Engineering

Year

2012

Author dc:creator

• Li, Xiang

• structural damage
• support vector
• Aerospace Engineering
• Structures and Materials

Repository record dc:identifier

https://commons.erau.edu/edt/92

OAI identifier oai:identifier

oai:commons.erau.edu:edt-1091