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Monitoring the Microgravity Environment Quality On-board the International Space Station Using Soft Computing TechniquesThis paper presents the preliminary performance results of the artificial intelligence monitoring system in full operational mode using near real time acceleration data downlinked from the International Space Station. Preliminary microgravity environment characterization analysis result for the International Space Station (Increment-2), using the monitoring system is presented. Also, comparison between the system predicted performance based on ground test data for the US laboratory "Destiny" module and actual on-orbit performance, using measured acceleration data from the U.S. laboratory module of the International Space Station is presented. Finally, preliminary on-orbit disturbance magnitude levels are presented for the Experiment of Physics of Colloids in Space, which are compared with on ground test data. The ground test data for the Experiment of Physics of Colloids in Space were acquired from the Microgravity Emission Laboratory, located at the NASA Glenn Research Center, Cleveland, Ohio. The artificial intelligence was developed by the NASA Glenn Principal Investigator Microgravity Services Project to help the principal investigator teams identify the primary vibratory disturbance sources that are active, at any moment of time, on-board the International Space Station, which might impact the microgravity environment their experiments are exposed to. From the Principal Investigator Microgravity Services' web site, the principal investigator teams can monitor via a dynamic graphical display, implemented in Java, in near real time, which event(s) is/are on, such as crew activities, pumps, fans, centrifuges, compressor, crew exercise, structural modes, etc., and decide whether or not to run their experiments, whenever that is an option, based on the acceleration magnitude and frequency sensitivity associated with that experiment. This monitoring system detects primarily the vibratory disturbance sources. The system has built-in capability to detect both known and unknown vibratory disturbance sources. Several soft computing techniques such as Kohonen's Self-Organizing Feature Map, Learning Vector Quantization, Back-Propagation Neural Networks, and Fuzzy Logic were used to design the system.
Document ID
20020082952
Acquisition Source
Glenn Research Center
Document Type
Technical Memorandum (TM)
Authors
Jules, Kenol
(NASA Glenn Research Center Cleveland, OH United States)
Lin, Paul P.
(Cleveland State Univ. Cleveland, OH United States)
Weiss, Daniel S.
(Harvard Univ. Cambridge, MA United States)
Date Acquired
September 7, 2013
Publication Date
August 1, 2002
Subject Category
Spacecraft Instrumentation And Astrionics
Report/Patent Number
NAS 1.15:211813
IAF-01-J.5.01
E-13509
NASA/TM-2002-211813
Meeting Information
Meeting: 52nd International Astronautical Congress
Location: Toulouse
Country: France
Start Date: October 1, 2001
End Date: October 5, 2001
Sponsors: Floral World Academy, International Astronautical Federation, Academy of Science, Art and Literature Academy
Funding Number(s)
PROJECT: RTOP 400-35-4C
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
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