Simulation-based expert system for nuclear-power-plant diagnostics

Applications of expert systems to the diagnostics of nuclear power plant accidents is considered. In this work, dynamic simulators, Kalman filtering, pattern recognition, fuzzy diagnostics and artificial intelligence are combined in a unique algorithm for diagnosing and analyzing nuclear plant transients targeted for use on-line and in real time. Knowledge-based reasoning is used to monitor plant data and hypothesize about the status of the plant. Fuzzy logic is employed as the inferencing mechanism and an implication scheme based on observations is developed and employed to handle scenarios involving competing failures. Hypothesis testing is performed by simulating the behavior of faulted components using numerical models. A simulation filter was developed based on the structure of the Kalman filter for systematically adjusting key model parameters to force agreement between the simulation and actual plant data. The unique feature of the simulation filter is that it operates only on the discrete time-series of inputs and associated outputs of a dynamic simulation program, thus admitting arbitrary system dynamics and being readily applicable to any system for which a simulation program for computing system states is available. Detailed simulation results of various nuclear power plant accident scenarios are presented to demonstrate the performance and robustness properties of the diagnostic algorithm developed