Damage identification with probabilistic neural networks

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This paper investigates the use of artificial neural networks (ANNs) to identify damage in mechanical systems. Two probabilistic neural networks (PNNs) are developed and used to judge whether or not damage has occurred in a specific mechanical system, based on experimental measurements. The first PNN is a classical type that casts Bayesian decision analysis into an ANN framework, it uses exemplars measured from the undamaged and damaged system to establish whether system response measurements of unknown origin come from the former class (undamaged) or the latter class (damaged). The second PNN establishes the character of the undamaged system in terms ... continued below

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6 p.

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Klenke, S.E. & Paez, T.L. December 1, 1995.

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  • Sandia National Laboratories
    Publisher Info: Sandia National Labs., Albuquerque, NM (United States)
    Place of Publication: Albuquerque, New Mexico

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Description

This paper investigates the use of artificial neural networks (ANNs) to identify damage in mechanical systems. Two probabilistic neural networks (PNNs) are developed and used to judge whether or not damage has occurred in a specific mechanical system, based on experimental measurements. The first PNN is a classical type that casts Bayesian decision analysis into an ANN framework, it uses exemplars measured from the undamaged and damaged system to establish whether system response measurements of unknown origin come from the former class (undamaged) or the latter class (damaged). The second PNN establishes the character of the undamaged system in terms of a kernel density estimator of measures of system response; when presented with system response measures of unknown origin, it makes a probabilistic judgment whether or not the data come from the undamaged population. The physical system used to carry out the experiments is an aerospace system component, and the environment used to excite the system is a stationary random vibration. The results of damage identification experiments are presented along with conclusions rating the effectiveness of the approaches.

Physical Description

6 p.

Notes

OSTI as DE96002782

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  • 14. international modal analysis conference, Dearborn, MI (United States), 12-15 Feb 1996

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  • Other: DE96002782
  • Report No.: SAND--95-2479C
  • Report No.: CONF-960238--3
  • Grant Number: AC04-94AL85000
  • Office of Scientific & Technical Information Report Number: 150953
  • Archival Resource Key: ark:/67531/metadc618527

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  • December 1, 1995

Added to The UNT Digital Library

  • June 16, 2015, 7:43 a.m.

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  • April 13, 2016, 1:19 p.m.

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Klenke, S.E. & Paez, T.L. Damage identification with probabilistic neural networks, article, December 1, 1995; Albuquerque, New Mexico. (digital.library.unt.edu/ark:/67531/metadc618527/: accessed September 22, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.