Nuclear power plant status diagnostics using an artificial neural network

Bartlett, EB; Uhrig, RE

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來源出版物：Nuclear Safety, 1991, 32(1): 68-79

Nuclear power plant status diagnostics using an artificial neural network

Bartlett, EB; Uhrig, RE

Abstract:In this work, nuclear power plant operating status recognition is investigated using a self-optimizing stochastic learning algorithm artificial neural network (ANN) with dynamic node architecture learning. The objective is to train the ANN to classify selected nuclear power plant accident conditions and assess the potential for future success in this area. The network is trained on normal operating conditions as well as on potentially unsafe conditions based on nuclear power plant training simulator-generated accident scenarios. These scenarios include hot-and cold-leg loss of coolant, control rod ejection, total loss of off-site power, main steamline break, main feed water line break, and steam generator tube leak accidents as well as the normal operating condition. Findings show that ANNs can be used to diagnose and classify nuclear power plant conditions with good results. Continued research work is indicated.

artificial neural network; fault diagnosis; nuclear power plant safety

來源出版物：The Lancet, 2013, 381(9881): 1916-1925

Abnormal event identification in nuclear power plants using a neural network and knowledge processing

Ohga, Y; Seki, H

Abstract:The combination of a neural network and knowledge processing have been used to identify abnormal events that cause a reactor to scram in a nuclear power plant. The neural network recognizes the abnormal event from the change pattern of analog data for state variables, and this result is confirmed from digital data using a knowledge base of plant status when each event occurs. The event identification method is tested using test data based on simulated results of a transient analysis program for boiling water reactors. It is confirmed that a neural network can identify an event in which it has been trained even when the plant conditions, such as fuel burnup, differ from those used in the training and when the analog data contain white noise. The network does not mistakenly identify the nontrained event as a trained one. The method is feasible for event identification, and knowledge processing improves the reliability of the identification.

關(guān)鍵詞：neural network; abnormal event identification; operation support

來源出版物：Nuclear Technology, 1993, 101(2): 159-167

Error prediction for a nuclear power plant fault-diagnostic advisor using neural networks

Kim, K; Bartlett, EB

Abstract:The objective of this research is to develop a fault-diagnostic advisor for nuclear power plant transients that is based on artificial neural networks. A method is described that provides an error bound and therefore a

figure of merit for the diagnosis provided by this advisor. The data used in the development of the advisor contain ten simulated anomalies for the San Onofre Nuclear Power Generating Station. The stacked generalization approach is used with two different partitioning schemes. The results of these partitioning schemes are compared. It is shown that the advisor is capable of recognizing all ten anomalies while providing estimated error bounds on each of its diagnoses.

關(guān)鍵詞：artificial neural networks; stacked generalization; verification and validation

來源出版物：Nuclear Technology, 1994, 108(2): 283-297

Survey of artificial intelligence methods for detection and identification of component faults in nuclear power plants

Reifman, J

Abstract:A comprehensive survey of computer-based systems that apply artificial intelligence methods to detect and identify component faults in nuclear power plants is presented. Classification criteria are established that categorize artificial intelligence diagnostic systems according to the types of computing approaches used (e.g., computing tools, computer languages, and shell and simulation programs), the types of methodologies employed (e.g., types of knowledge, reasoning and inference mechanisms, and diagnostic approach), and the scope of the system. The major issues of process diagnostics and computer-based diagnostic systems are identified and cross-correlated with the various categories used for classification. Ninety-five publications are reviewed.

關(guān)鍵詞：artificial intelligence; nuclear power plants; diagnosis

來源出版物：Nuclear Technology, 1977, 119(1): 76-97

Potential application of neural networks to the operation of nuclear power plants

Uhrig, RE

The application of neural networks, a rapidly evolving technology used extensively in defense applications, to some of the problems of operating nuclear power plants is a logical complement to the expert systems currently being introduced in some of those plants. The potential applications of neural networks include, but are not limited to: Diagnosing specific abnormal conditions. Identifying nonlinear dynamics and transients. Detecting the change of mode of operation. Controlling temperature and pressure during start-up. Validating signals. Plant-wide monitoring using autoassociative neural networks. Monitoring of check valves. Modeling the plant thermodynamics to increase efficiency. Emulating core reload calculations. Analyzing temporal sequences in the U.S. Nuclear Regulatory Commission Licensee Event Reports. Monitoring plant parameters. Analyzing vibrations in plants and rotating machinery. The work on such applications indicates that neural networks alone, or in conjunction with other advanced technologies, have the potential to enhance the safety, reliability, and operability of nuclear power plants.