Sensor validation in power plants using adaptive backpropagation neural network
Signal validation and process monitoring problems in many cases require the prediction of one or more process variables in a system. The feasibility of using neural networks to characterize one variable as a function of other related variables is studied. The Backpropagation Network (BPN) is used to develop ``models`` of signals from both a commercial power plant and the EBR-II. Several innovations are made in the algorithm, the most significant of which is the progressive adjustment of the sigmoidal threshold function and weight updating terms, thus leading to the designation ``Adaptive`` Backpropagation Neural Network. The estimation of system variables is performed traditionally using either physical models or empirical models. The prediction of system variables is important in control systems for validating instrumentation outputs and for process monitoring. The model-based prediction assumes a fixed structure for characterizing steady-state or dynamic relationship among process variables. The applications to large and complex systems require more time in order to get an accurate model. Since our goal is to relate signals in a subsystem of a plant, such a relationship can be developed by using neural network ``models`` which provide results faster than model-based techniques. Both steady-state and transient behavior can be incorporated into the network during training.
- Research Organization:
- Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- FG07-88ER12824
- OSTI ID:
- 10107647
- Report Number(s):
- CONF-900143-39; ON: DE93002959
- Resource Relation:
- Conference: Institute for Electrical and Electronics Engineers (IEEE) nuclear science symposium,San Francisco, CA (United States),15-19 Jan 1990; Other Information: PBD: [1990]
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
EBR-2 REACTOR
REACTOR MONITORING SYSTEMS
NEURAL NETWORKS
TRAINING
VALIDATION
NUCLEAR POWER PLANTS
SIGNAL CONDITIONING
220600
220400
990200
RESEARCH
TEST
TRAINING
PRODUCTION
IRRADIATION
MATERIALS TESTING REACTORS
CONTROL SYSTEMS
MATHEMATICS AND COMPUTERS