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Vibrational Analysis of Engine Components Using Neural-Net Processing and Electronic HolographyThe use of computational-model trained artificial neural networks to acquire damage specific information from electronic holograms is discussed. A neural network is trained to transform two time-average holograms into a pattern related to the bending-induced-strain distribution of the vibrating component. The bending distribution is very sensitive to component damage unlike the characteristic fringe pattern or the displacement amplitude distribution. The neural network processor is fast for real-time visualization of damage. The two-hologram limit makes the processor more robust to speckle pattern decorrelation. Undamaged and cracked cantilever plates serve as effective objects for testing the combination of electronic holography and neural-net processing. The requirements are discussed for using finite-element-model trained neural networks for field inspections of engine components. The paper specifically discusses neural-network fringe pattern analysis in the presence of the laser speckle effect and the performances of two limiting cases of the neural-net architecture.
Document ID
19980206033
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Decker, Arthur J.
(NASA Lewis Research Center Cleveland, OH United States)
Fite, E. Brian
(NASA Lewis Research Center Cleveland, OH United States)
Mehmed, Oral
(NASA Lewis Research Center Cleveland, OH United States)
Thorp, Scott A.
(NASA Lewis Research Center Cleveland, OH United States)
Date Acquired
August 18, 2013
Publication Date
May 1, 1998
Subject Category
Cybernetics
Distribution Limits
Public
Copyright
Public Use Permitted.
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