1,720,962 research outputs found
APPLICATION OF NEURAL NETWORKS TO A CONNECTIONIST EXPERT SYSTEM FOR TRANSIENT IDENTIFICATION IN NUCLEAR-POWER-PLANTS
No full textExpert systems that have neural networks for their knowledge bases are called connectionist expert systems. Several powerful advantages of connectionist expert systems over conventional rule-based expert systems are discussed. The backpropagation network (BPN) algorithm is applied to the connectionist expert system for the identification of transients in nuclear powerplants. In this approach, the transient is identified by mapping or associating patterns of symptom input vectors to patterns representing transient conditions. The general mapping capability of the neural network allows one to identify a transient easily. A number of case studies are performed with emphasis on the applicability of the neural network to the classification problems. Based on the case studies, the BPN algorithm can identify the transient well, although untrained, incomplete, sensor-failed, or time-varying symptoms are given. Also, multiple transients are easily identified with a given symptom input vector
DEVELOPMENT STRATEGIES OF AN EXPERT SYSTEM FOR MULTIPLE ALARM PROCESSING AND DIAGNOSIS IN NUCLEAR-POWER-PLANTS
No full textThis paper describes the development strategies of a prototype expert system, called ESAPD, for multiple alarm processing and diagnosis in nuclear power plants. The main objectives of the system are to assist operators to identify a primary causal alarm among multiple fired alarms and to diagnose the plant malfunction quickly. The overall plant-wide diagnosis is performed at the alarm processing stage which can identify a primary causal alarm and can diagnose possible failure modes and failed systems, and automatic interlock actions. The knowledge base for the alarm processing is represented as object-oriented concepts. The specific root cause diagnosis for the primary causal alarm can be performed at the alarm diagnosis stage. The system can provide operators with the possible causes of the primary causal alarm, emergency actions, and follow-up treatments. The diagnostic method adopted in this system is a ''hypothesize and test'' paradigm
THERMAL POWER PREDICTION OF NUCLEAR-POWER-PLANT USING NEURAL NETWORK AND PARITY SPACE MODEL
No full textA power prediction system was developed using an artificial neural network paradigm that was combined with a parity space signal validation technique. The parity space signal validation algorithm for the input preprocessing and the backpropagation network algorithm for the network learning are used for the power prediction system. A number of case studies were performed with emphasis on the applicability of the network in a steady-state high power level. The studies reveal that these algorithms can precisely predict the thermal power in a nuclear power plant. It also shows that the error signals resulting from instrumentation problems, even when the signals comprising various patterns are noisy or incomplete, can be properly treated
APPLICATION OF NEURAL NETWORKS TO MULTIPLE ALARM PROCESSING AND DIAGNOSIS IN NUCLEAR-POWER-PLANTS
No full textWe present the feasibility study of multiple alarm processing and diagnosis using neural networks. The back-propagation network (BPN) algorithm is applied to the training of multiple alarm patterns for the identification of faults in a reactor coolant pump (RCP) system. The general mapping capability of the neural network enables to identify a fault easily. A number of case studies are performed with emphasis on the applicability of the neural network to the pattern recognition of multiple alarms. Based on the case studies, the neural network can identify the cause of multiple alarms well, although untrained, incomplete/sensor-failed or time-varying alarm symptoms are given. Also, multiple faults are easily identified with a given alarm pattern
Application of Neural Networks to Connectionist Expert System for Identification of Transients in Nuclear Power Plants
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DIAGNOSTIC STRATEGIES OF A PROTOTYPE EXPERT SYSTEM FOR MALFUNCTION DIAGNOSIS OF PRIMARY-SIDE SYSTEMS IN NUCLEAR-POWER-PLANT
No full textA prototype expert system, called NSSS-DS, has been developed for the diagnosis of three main systems (the rod control system, the reactor coolant pumps (RCPs) and the pressurizer) in the primary system of the Kori-2 nuclear power plant in Korea. This system diagnoses system-malfunction quickly and offers appropriate guidance to operators. This system uses rule-based deduction with certainty factor operation. The diagnostic symptoms include alarms, indication lamps, parameter values and valve line-up that can be received at the main control room. The overall plant-wide diagnosis is performed by the main control part which processes the fired multi-alarm information and diagnoses possible transients and failed systems. The specific diagnosis of the three main systems is performed followed by the diagnostic results of the main control part. The application to these systems is described from the point of view of diagnostic strategies
Development of an Expert System for Malfuction Diagnosis of Primary System in Nuclear Power Plant
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