Air Leak Material Identification in Pressurized Space Vehicles using a Convolutional Neural Network

被引：0
|
作者：
Bundy, Kenneth R. [1 ]
Abedi, Ali [1 ]
机构：
[1] Univ Maine, Dept Elect & Comp Engn, Wireless Sensor Networks WiSe Net Lab, Orono, ME 04473 USA
来源：
2017 IEEE INTERNATIONAL CONFERENCE ON WIRELESS FOR SPACE AND EXTREME ENVIRONMENTS (WISEE) | 2017年
基金：
美国国家航空航天局;
关键词：
D O I：
暂无
中图分类号：
V [航空、航天];
学科分类号：
08 ; 0825 ;
摘要：
Pressurized space vehicles of all types are at risk of depressurization due to leaking air. Leaks may be caused by micro-meteor and orbital debris (MMOD) impact or structural aging and failure overtime. This paper addresses the issue of leak type detection by analyzing airborne ultrasonic waves using a Convolutional Neural Network. Depending on the vessel material, size of the leak, and pressure gradient, different waveforms are produced. Once a large number of samples have been recorded, the resulting data is used for the training of a Convolutional Neural Network for leak classification.
引用
收藏
页码：150 / 152
页数：3
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