Machine condition monitoring using neural networks and the likelihood function

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Description

A model-based technique incorporating neural networks has been developed for process monitoring. The technique is intended for processes where the uncertainty in the reference model is larger than desired but where process measurements providing additional information about the behavior of the system are available. This data is used to reduce the uncertainty of the model. The technique has been implemented in a real-time system for monitoring operational changes of mechanical equipment for use in predictive maintenance applications. Tests on a peristaltic pump were conducted and demonstrate the advantages of the proposed technique.

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9 p.

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Vilim, R.B.; Garcia, H.E. & Chen, F.W. September 1, 1997.

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Description

A model-based technique incorporating neural networks has been developed for process monitoring. The technique is intended for processes where the uncertainty in the reference model is larger than desired but where process measurements providing additional information about the behavior of the system are available. This data is used to reduce the uncertainty of the model. The technique has been implemented in a real-time system for monitoring operational changes of mechanical equipment for use in predictive maintenance applications. Tests on a peristaltic pump were conducted and demonstrate the advantages of the proposed technique.

Physical Description

9 p.

Notes

OSTI as DE97053708

Source

  • ANNIE `97: neural networks, fuzzy logic, data mining and evolutionary programming for designing smart engineering systems, Rolla, MO (United States), 9-12 Nov 1997

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  • Other: DE97053708
  • Report No.: ANL/RA/CP--93780
  • Report No.: CONF-971189--
  • Grant Number: W-31-109-ENG-38
  • Office of Scientific & Technical Information Report Number: 561163
  • Archival Resource Key: ark:/67531/metadc693484

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Office of Scientific & Technical Information Technical Reports

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  • September 1, 1997

Added to The UNT Digital Library

  • Aug. 14, 2015, 8:43 a.m.

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  • May 16, 2016, 4:56 p.m.

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Vilim, R.B.; Garcia, H.E. & Chen, F.W. Machine condition monitoring using neural networks and the likelihood function, article, September 1, 1997; Illinois. (digital.library.unt.edu/ark:/67531/metadc693484/: accessed October 18, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.