Date of Award
Summer 6-2006
Document Type
Thesis - Open Access
Degree Name
Master of Science in Aerospace Engineering
Department
Aerospace Engineering
Committee Chair
Eric v. K. Hill
Committee Member
Eric Perrell
Committee Member
Seenithamby Sivasundaram
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.
Scholarly Commons Citation
Karl, Justin O., "Filtering of Acoustic Emission Data Through Principal Frequency Component Extraction" (2006). Master's Theses - Daytona Beach. 93.
https://commons.erau.edu/db-theses/93
