Neural-network-based state of health diagnostics for an automated radioxenon sampler/analyzer

Paul E. Keller; Lars J. Kangas; James C. Hayes; Brian T. Schrom; Reynold Suarez; Charles W. Hubbard; Tom R. Heimbigner; Justin I. McIntyre

doi:10.1117/12.817613

29 April 2009 Neural-network-based state of health diagnostics for an automated radioxenon sampler/analyzer

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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Proceedings Volume 7347, Evolutionary and Bio-Inspired Computation: Theory and Applications III; 73470Y (2009) https://doi.org/10.1117/12.817613
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

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.

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Paul E. Keller, Lars J. Kangas, James C. Hayes, Brian T. Schrom, Reynold Suarez, Charles W. Hubbard, Tom R. Heimbigner, and Justin I. McIntyre "Neural-network-based state of health diagnostics for an automated radioxenon sampler/analyzer", Proc. SPIE 7347, Evolutionary and Bio-Inspired Computation: Theory and Applications III, 73470Y (29 April 2009); https://doi.org/10.1117/12.817613

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KEYWORDS

Sensors

Principal component analysis

Diagnostics

Data modeling

Systems modeling

Neurons

Analytical research

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