DocumentCode :
3286525
Title :
Diagnostic models for sensor measurements in rocket engine tests
Author :
Russell, Michael ; Lecakes, George, Jr. ; Mandayam, Shreekanth ; Jensen, Scott
Author_Institution :
Dept. of Electr. & Comput. Eng., Rowan Univ., Glassboro, NJ, USA
fYear :
2009
fDate :
25-28 Oct. 2009
Firstpage :
764
Lastpage :
769
Abstract :
This paper presents our ongoing work in the area of using virtual reality (VR) environments for the Integrated Systems Health Management (ISHM) of rocket engine test stands. Specifically, this paper focuses on the development of an intelligent valve model that integrates into the control center at NASA Stennis Space Center. The intelligent valve model integrates diagnostic algorithms and 3D visualizations in order to diagnose and predict failures of a large linear actuator valve (LLAV). The diagnostic algorithm uses auto-associative neural networks to predict expected values of sensor data based on the current readings. The predicted values are compared with the actual values and drift is detected in order to predict failures before they occur. The data is then visualized in a VR environment using proven methods of graphical, measurement, and health visualization. The data is also integrated into the control software using an ActiveX plug-in.
Keywords :
actuators; control engineering computing; data visualisation; fault diagnosis; intelligent sensors; neural nets; rocket engines; valves; virtual reality; 3D visualizations; ActiveX plug-in; Integrated Systems Health Management; NASA Stennis Space Center; auto-associative neural networks; control center; diagnostic algorithms; intelligent valve model; large linear actuator valve; rocket engine tests; sensor measurements; virtual reality environments; Data visualization; Engines; Environmental management; Intelligent actuators; Intelligent sensors; NASA; Rockets; System testing; Valves; Virtual reality;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Sensors, 2009 IEEE
Conference_Location :
Christchurch
ISSN :
1930-0395
Print_ISBN :
978-1-4244-4548-6
Electronic_ISBN :
1930-0395
Type :
conf
DOI :
10.1109/ICSENS.2009.5398535
Filename :
5398535
Link To Document :
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