Early detection of incipient faults in power plants using accelerated neural network learning
- Texas A M Univ., College Station (United States)
- Qantixx, Inc., Houston, TX (United States)
An important aspect of power plant automation is the development of computer systems able to detect and isolate incipient (slowly developing) faults at the earliest possible stages of their occurrence. In this paper, the development and testing of such a fault detection scheme is presented based on recognition of sensor signatures during various failure modes. An accelerated learning algorithm, namely adaptive backpropagation (ABP), has been developed that allows the training of a multilayer perceptron (MLP) network to a high degree of accuracy, with an order of magnitude improvement in convergence speed. An artificial neural network (ANN) has been successfully trained using the ABP algorithm, and it has been extensively tested with simulated data to detect and classify incipient faults of various types and severity and in the presence of varying sensor noise levels.
- OSTI ID:
- 6981648
- Report Number(s):
- CONF-921102-; CODEN: TANSAO
- Journal Information:
- Transactions of the American Nuclear Society; (United States), Vol. 66; Conference: Joint American Nuclear Society (ANS)/European Nuclear Society (ENS) international meeting on fifty years of controlled nuclear chain reaction: past, present, and future, Chicago, IL (United States), 15-20 Nov 1992; ISSN 0003-018X
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
NUCLEAR POWER PLANTS
AUTOMATION
MONITORING
ALGORITHMS
ARTIFICIAL INTELLIGENCE
FAILURE MODE ANALYSIS
LEARNING
NEURAL NETWORKS
MATHEMATICAL LOGIC
NUCLEAR FACILITIES
POWER PLANTS
SYSTEM FAILURE ANALYSIS
SYSTEMS ANALYSIS
THERMAL POWER PLANTS
220900* - Nuclear Reactor Technology- Reactor Safety
990200 - Mathematics & Computers