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Fault Analysis of Space Station DC Power Systems-Using Neural Network Adaptive Wavelets to Detect FaultsThis paper describes the application of neural network adaptive wavelets for fault diagnosis of space station power system. The method combines wavelet transform with neural network by incorporating daughter wavelets into weights. Therefore, the wavelet transform and neural network training procedure become one stage, which avoids the complex computation of wavelet parameters and makes the procedure more straightforward. The simulation results show that the proposed method is very efficient for the identification of fault locations.
Document ID
19970012903
Acquisition Source
Legacy CDMS
Document Type
Technical Memorandum (TM)
Authors
Momoh, James A. (Howard Univ. Washington, DC United States)
Wang, Yanchun (Howard Univ. Washington, DC United States)
Dolce, James L. (NASA Lewis Research Center Cleveland, OH United States)
Date Acquired
September 6, 2013
Publication Date
February 1, 1997
Subject Category
Cybernetics
Report/Patent Number
NAS 1.15:107389
E-10585
NASA-TM-107389
Meeting Information
Meeting: Space Technology and Applications International Forum
Location: Albuquerque, NM
Country: United States
Start Date: January 26, 1997
End Date: January 30, 1997
Sponsors: Department of the Air Force, Department of Energy, Defense Special Weapons Agency, NASA Headquarters
Accession Number
97N17029
Funding Number(s)
PROJECT: RTOP 547-20-00
CONTRACT_GRANT: NAG3-1426
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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