The Hilbert-Huang Transform: A Theoretical Framework and Applications to Leak Identification in Pressurized Space Modules

Any manned space mission must provide breathable air to its crew. For this reason, air leaks in spacecraft pose a danger to the mission and any astronauts on board. The purpose of this work is twofold: the first is to address the issue of air pressure loss from leaks in spacecraft. Air leaks present a danger to spacecraft crew, and so a method of finding air leaks when they occur is needed. Most leak detection systems localize the leak in some way. Instead, we address the identification of air leaks in a pressurized space module, we aim to determine the material in which the leak occurs. This is done with methods centered on statistics and machine learning. In addition to these findings, we investigate one of the methods used in the leak identification section, the Hilbert-Huang Transform. This method has seen many demonstrations of its effectiveness, however this method lacks a solid theoretical framework. We make some contributions to the background of the Hilbert-Huang Transform