Abstract
A signal validation system, based on several parallel signal processing modules, is being developed at the University of Tennessee. The major modules perform (1) general consistency checking (GCC) of a set of redundant measurements, (2) multivariate data-driven modeling of dynamic signal components for maloperation detection, (3) process empirical modeling for prediction and redundancy generation, (4) jump, pulse, noise detection, and (5) an expert system for qualitative signal validation. A central database stores information related to sensors, diagnostics rules, past system performance, subsystem models, etc. We are primarily concerned with signal validation during steady-state operation and slow degradations. In general, the different modules will perform signal validation during all operating conditions. The techniques have been successfully tested using PWR steam generator simulation, and efforts are currently underway in applying the techniques to Millstone-III operational data. These methods could be implemented in advanced reactors, including advanced liquid metal reactors.
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© 1988 Plenum Press, New York
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Upadhyaya, B.R., Kerlin, T.W., Glöckler, O., Frei, Z., Qualls, L., Morgenstern, V. (1988). An Integrated Approach for Signal Validation in Nuclear Power Plants. In: Majumdar, M.C., Majumdar, D., Sackett, J.I. (eds) Artificial Intelligence and Other Innovative Computer Applications in the Nuclear Industry. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1009-9_21
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DOI: https://doi.org/10.1007/978-1-4613-1009-9_21
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