Application of knowledge-based network processing to automated gas chromatography data interpretation

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A method of translating a two-way table of qualified symptom/cause relationships into a four layer Expert Network for diagnosis of machine or sample preparation failure for Gas Chromatography is presented. This method has proven to successfully capture an expert`s ability to predict causes of failure in a Gas Chromatograph based on a small set of symptoms, derived from a chromatogram, in spite of poorly defined category delineations and definitions. In addition, the resulting network possesses the advantages inherent in most neural networks: the ability to function correctly in the presence of missing or uncertain inputs and the ability to improve ... continued below

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8 p.

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Levis, A.P.; Timpany, R.G. & Klotter, D.A. October 1, 1995.

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  • EG & G, Inc.
    Publisher Info: EG and G Idaho, Inc., Idaho Falls, ID (United States)
    Place of Publication: Idaho Falls, Idaho

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A method of translating a two-way table of qualified symptom/cause relationships into a four layer Expert Network for diagnosis of machine or sample preparation failure for Gas Chromatography is presented. This method has proven to successfully capture an expert`s ability to predict causes of failure in a Gas Chromatograph based on a small set of symptoms, derived from a chromatogram, in spite of poorly defined category delineations and definitions. In addition, the resulting network possesses the advantages inherent in most neural networks: the ability to function correctly in the presence of missing or uncertain inputs and the ability to improve performance through data-based training procedures. Acquisition of knowledge from the domain experts produced a group of imprecise cause-to-symptom relationships. These are reproduced as parallel pathways composed of Symptom-Filter-Combination-Cause node chains in the network representation. Each symptom signal is passed through a Filter node to determine if the signal should be interpreted as positive or negative evidence and then modified according to the relationship established by the domain experts. The signals from several processed symptoms are then combined in the Combination node(s) for a given cause. The resulting value is passed to the Cause node and the highest valued Cause node is then selected as the most probable cause of failure.

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8 p.

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INIS; OSTI as DE96001620

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  • SPIE international symposium on aerospace/defense sensing and dual-use photonics, Orlando, FL (United States), 17-21 Apr 1995

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  • Other: DE96001620
  • Report No.: INEL--95/00041
  • Report No.: CONF-950472--1
  • Grant Number: AC07-94ID13223
  • Office of Scientific & Technical Information Report Number: 116709
  • Archival Resource Key: ark:/67531/metadc621427

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  • October 1, 1995

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  • June 16, 2015, 7:43 a.m.

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  • April 26, 2016, 4:47 p.m.

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Levis, A.P.; Timpany, R.G. & Klotter, D.A. Application of knowledge-based network processing to automated gas chromatography data interpretation, article, October 1, 1995; Idaho Falls, Idaho. (digital.library.unt.edu/ark:/67531/metadc621427/: accessed September 20, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.