Rapid editing of acoustic emission (AE) data is required in order to make real-time acoustic emission flaw growth systems a viable testing method for materials and setups that contain noisy signals. It was hypothesized that extracting major frequency components from the acoustic emission signal would therefore provide a representative acoustic signature of the major waveforms occurring due to defect growth This research has verified that the aforementioned filtering technique does, in fact, extract a representative signal from the composite and metal specimens utilized herein These findings were verified both through visual analysis of the data as well as the low error occurrence in backpropagation neural network predictions and good classification in self-organizing map type neural networks applied to the testing data.
","abstract_html":"<p>Rapid editing of acoustic emission (AE) data is required in order to make real-time acoustic emission flaw growth systems a viable testing method for materials and setups that contain noisy signals. It was hypothesized that extracting major frequency components from the acoustic emission signal would therefore provide a representative acoustic signature of the major waveforms occurring due to defect growth This research has verified that the aforementioned filtering technique does, in fact, extract a representative signal from the composite and metal specimens utilized herein These findings were verified both through visual analysis of the data as well as the low error occurrence in backpropagation neural network predictions and good classification in self-organizing map type neural networks applied to the testing data.</p>","abstract_has_math":false,"creators":["Karl, Justin O."],"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":["Eric v. K. Hill","Eric Perrell","Seenithamby Sivasundaram"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2006,"date_issued":"2006-06-01T07:00:00Z","date_published":"2006-06-01T07:00:00Z","updated_at":"2026-07-27T19:25:37Z","subjects":["acoustic emission","component extraction","Aerospace Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/db-theses/93","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Eric v. K. Hill","Eric Perrell","Seenithamby Sivasundaram"]},{"key":"dc:creator","label":"Author","values":["Karl, Justin O."]}]},{"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":["acoustic emission","component extraction","Aerospace Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/db-theses/93"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Rapid editing of acoustic emission (AE) data is required in order to make real-time acoustic emission flaw growth systems a viable testing method for materials and setups that contain noisy signals. It was hypothesized that extracting major frequency components from the acoustic emission signal would therefore provide a representative acoustic signature of the major waveforms occurring due to defect growth This research has verified that the aforementioned filtering technique does, in fact, extract a representative signal from the composite and metal specimens utilized herein These findings were verified both through visual analysis of the data as well as the low error occurrence in backpropagation neural network predictions and good classification in self-organizing map type neural networks applied to the testing data.
"]},{"key":"dc:title","label":"Title","values":["Filtering of Acoustic Emission Data Through Principal Frequency Component Extraction"]}]}],"canonical_facts":{"dc:contributor":["Eric v. K. Hill","Eric Perrell","Seenithamby Sivasundaram"],"dc:creator":["Karl, Justin O."],"dc:description.abstract":["Rapid editing of acoustic emission (AE) data is required in order to make real-time acoustic emission flaw growth systems a viable testing method for materials and setups that contain noisy signals. It was hypothesized that extracting major frequency components from the acoustic emission signal would therefore provide a representative acoustic signature of the major waveforms occurring due to defect growth This research has verified that the aforementioned filtering technique does, in fact, extract a representative signal from the composite and metal specimens utilized herein These findings were verified both through visual analysis of the data as well as the low error occurrence in backpropagation neural network predictions and good classification in self-organizing map type neural networks applied to the testing data.
"],"dc:identifier":["https://commons.erau.edu/db-theses/93"],"dc:subject":["acoustic emission","component extraction","Aerospace Engineering"],"dc:title":["Filtering of Acoustic Emission Data Through Principal Frequency Component Extraction"],"thesis:degree_discipline":["Aerospace Engineering"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science in Aerospace Engineering"]},"updated_at":"2026-07-27T19:25:37Z"}