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Proceedings Paper

Neural-network-based state of health diagnostics for an automated radioxenon sampler/analyzer
Author(s): Paul E. Keller; Lars J. Kangas; James C. Hayes; Brian T. Schrom; Reynold Suarez; Charles W. Hubbard; Tom R. Heimbigner; Justin I. McIntyre
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Paper Abstract

Artificial neural networks (ANNs) are used to determine the state-of-health (SOH) of the Automated Radioxenon Analyzer/Sampler (ARSA). ARSA is a gas collection and analysis system used for non-proliferation monitoring in detecting radioxenon released during nuclear tests. SOH diagnostics are important for automated, unmanned sensing systems so that remote detection and identification of problems can be made without onsite staff. Both recurrent and feed-forward ANNs are presented. The recurrent ANN is trained to predict sensor values based on current valve states, which control air flow, so that with only valve states the normal SOH sensor values can be predicted. Deviation between modeled value and actual is an indication of a potential problem. The feed-forward ANN acts as a nonlinear version of principal components analysis (PCA) and is trained to replicate the normal SOH sensor values. Because of ARSA's complexity, this nonlinear PCA is better able to capture the relationships among the sensors than standard linear PCA and is applicable to both sensor validation and recognizing off-normal operating conditions. Both models provide valuable information to detect impending malfunctions before they occur to avoid unscheduled shutdown. Finally, the ability of ANN methods to predict the system state is presented.

Paper Details

Date Published: 29 April 2009
PDF: 9 pages
Proc. SPIE 7347, Evolutionary and Bio-Inspired Computation: Theory and Applications III, 73470Y (29 April 2009); doi: 10.1117/12.817613
Show Author Affiliations
Paul E. Keller, Pacific Northwest National Lab. (United States)
Lars J. Kangas, Pacific Northwest National Lab. (United States)
James C. Hayes, Pacific Northwest National Lab. (United States)
Brian T. Schrom, Pacific Northwest National Lab. (United States)
Reynold Suarez, Pacific Northwest National Lab. (United States)
Charles W. Hubbard, Pacific Northwest National Lab. (United States)
Tom R. Heimbigner, Pacific Northwest National Lab. (United States)
Justin I. McIntyre, Pacific Northwest National Lab. (United States)


Published in SPIE Proceedings Vol. 7347:
Evolutionary and Bio-Inspired Computation: Theory and Applications III
Teresa H. O'Donnell; Misty Blowers; Kevin L. Priddy, Editor(s)

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