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

Alan P. Levis; Robert G. Timpany; Wayne E. Austad; John W. Elling; Jamie J. Ferguson; Douglas Alan Klotter; Susan I. Hruska

doi:10.1117/12.205137

6 April 1995 Application of knowledge-based network processing to automated gas chromatography data interpretation

Alan P. Levis, Robert G. Timpany, Wayne E. Austad, John W. Elling, Jamie J. Ferguson, Douglas Alan Klotter, Susan I. Hruska

Author Affiliations +

Proceedings Volume 2492, Applications and Science of Artificial Neural Networks; (1995) https://doi.org/10.1117/12.205137
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

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.

Citation Download Citation

Alan P. Levis, Robert G. Timpany, Wayne E. Austad, John W. Elling, Jamie J. Ferguson, Douglas Alan Klotter, and Susan I. Hruska "Application of knowledge-based network processing to automated gas chromatography data interpretation", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); https://doi.org/10.1117/12.205137

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