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Accelerated Test MethodsNeural network systems were evaluated for use in predicting wear of mechanical systems. Three different neural network software simulation packages were utilized in order to create models of tribological wear tests. Representative simple, medium, and high complexity simulation packages were selected. Pin-on-disk, rub shoe, and four-ball tribological test data was used for training, testing, and verification of the neural network models. Results showed mixed success. The neural networks were able to predict results with some accuracy if the number of input variables was low or the amount of training data was high. Increased neural network complexity resulted in more accurate results, however there was a point of diminishing return. Medium complexity models were the best trade off between accuracy and computing time requirements. A NASA Technical Memorandum and a Society of Tribologists and Lubrication Engineers paper are being published which detail the work.
Document ID
19990009629
Acquisition Source
Legacy CDMS
Document Type
Contractor or Grantee Report
Authors
Jansen, Ralph (Ohio Aerospace Inst. Brook Park, OH United States)
Date Acquired
September 6, 2013
Publication Date
January 1, 1995
Subject Category
Cybernetics
Funding Number(s)
CONTRACT_GRANT: NCC3-361
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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