국내 최대 기계 및 로봇 연구정보

기술보고서

기술보고서 게시판 내용
타이틀	The Application of Neural Networks to the SSME Startup Transient
저자	Claudia M. Meyer and William A. Maul
Keyword	Neural nets; Space shuttle main engine; Test firing
URL	http://gltrs.grc.nasa.gov/reports/1991/CR-187138.pdf
보고서번호	NASA CR-187138
발행년도	1991
출처	NASA Glenn Research Center
ABSTRACT	Feedforward neural networks were used to model three parameters during the Space Shuttle Main Engine startup transient. The three parameters were the main combustion chamber pressure, a controlled parameter; the high pressure oxidizer turbine discharge temperature, a redlined parameter; and the high pressure fuel pump discharge pressure, a failureindicating performance parameter. Network inputs consisted of time windows of data from engine measurements that correlated highly to the modeled parameter. A standard backpropagation algorithm was used to train the feedforward networks on two nominal firings. Each trained network was validated with four additional nominal firings. For all three parameters, the neural networks were able to accurately predict the data in the validation sets as well as the training set.