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.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Li, Xiang"],"institution":null,"degree_name":"Master of Science in Aerospace Engineering","degree_level":"Thesis - Open Access","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012-10-01T07:00:00Z","date_published":"2012-10-01T07:00:00Z","updated_at":"2026-07-27T19:26:02Z","subjects":["structural damage","support vector","Aerospace Engineering","Structures and Materials"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/92","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Li, Xiang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Aerospace Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["structural damage","support vector","Aerospace Engineering","Structures and Materials"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/92"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Structural Damage Classification using Support Vector Machines"]}]}],"canonical_facts":{"dc:creator":["Li, Xiang"],"dc:description.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.
"],"dc:identifier":["https://commons.erau.edu/edt/92"],"dc:subject":["structural damage","support vector","Aerospace Engineering","Structures and Materials"],"dc:title":["Structural Damage Classification using Support Vector Machines"],"thesis:degree_discipline":["Aerospace Engineering"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science in Aerospace Engineering"]},"updated_at":"2026-07-27T19:26:02Z"}