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ANALYSIS OF COMPLEX SYSTEMS USING NEURAL NETWORKS

Robert E. Uhrig
Department of Nuclear Engineering, University of Tennessee, Knoxville, TN 37996-2300 and Instrumentation and Control Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6005

Sinopsis

The application of neural networks, alone or in conjunction with other advanced technologies (expert systems, fuzzy logic, and/or genetic algorithms), to some of the problems of complex engineering systems has the potential to enhance the safety, reliability, and operability of these systems. Typically, the measured variables from the systems are analog variables that must be sampled and normalized to expected peak values before they are introduced into neural networks. Often data must be processed to put it into a form more acceptable to the neural network (e.g., a fast Fourier transformation of the time-series data to produce a spectral plot of the data). Specific applications described include: 1) Diagnostics: State of the Plant, 2) Hybrid System for Transient Identification, 3) Sensor Validation, 4) Plant-Wide Monitoring, 5) Monitoring of Performance and Efficiency, and 6) Analysis of Vibrations. Although the specific examples described deal with nuclear power plants or their subsystems, the techniques described can be applied to a wide variety of complex engineering systems.

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