Fault diagnosis in nuclear power plants using an artificial neural network technique
- National Tsing-Hua Univ., Hsinchu (Taiwan, Province of China)
- Institute of Safety Technology, Garching (Germany)
Application of artificial intelligence (AI) computational techniques, such as expert systems, fuzzy logic, and neural networks in diverse areas has taken place extensively. In the nuclear industry, the intended goal for these AI techniques is to improve power plant operational safety and reliability. As a computerized operator support tool, the artificial neural network (ANN) approach is an emerging technology that currently attracts a large amount of interest. The ability of ANNs to extract the input/output relation of a complicated process and the superior execution speed of a trained ANN motivated this study. The goal was to develop neural networks for sensor and process faults diagnosis with the potential of implementing as a component of a real-time operator support system LYDIA, early sensor and process fault detection and diagnosis.
- OSTI ID:
- 7128927
- Report Number(s):
- CONF-931160-; CODEN: TANSAO
- Journal Information:
- Transactions of the American Nuclear Society; (United States), Vol. 69; Conference: American Nuclear Society (ANS) winter meeting, San Francisco, CA (United States), 14-18 Nov 1993; ISSN 0003-018X
- Country of Publication:
- United States
- Language:
- English
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Nuclear power plant fault-diagnosis using artificial neural networks
Nuclear power plant fault-diagnosis using artificial neural networks
Related Subjects
NEURAL NETWORKS
FUZZY LOGIC
NUCLEAR POWER PLANTS
SYSTEM FAILURE ANALYSIS
ARTIFICIAL INTELLIGENCE
DIAGNOSTIC TECHNIQUES
EXPERT SYSTEMS
MATHEMATICAL LOGIC
NUCLEAR FACILITIES
POWER PLANTS
SYSTEMS ANALYSIS
THERMAL POWER PLANTS
220900* - Nuclear Reactor Technology- Reactor Safety
220400 - Nuclear Reactor Technology- Control Systems