The importance of input variables to a neural network fault-diagnostic system for nuclear power plants | Zendy

T.L. Lanc | Zendy

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The importance of input variables to a neural network fault-diagnostic system for nuclear power plants

Author(s) -

T.L. Lanc

Publication year - 1992

Language(s) - English

Resource type - Reports

DOI - 10.2172/6686435

Subject(s) - scram , nuclear power , operator (biology) , fault (geology) , power (physics) , nuclear power plant , reliability engineering , computer science , signal (programming language) , engineering , risk analysis (engineering) , business , nuclear engineering , nuclear physics , biochemistry , chemistry , physics , repressor , quantum mechanics , seismology , transcription factor , gene , programming language , geology

This thesis explores safety enhancement for nuclear power plants. Emergency response systems currently in use depend mainly on automatic systems engaging when certain parameters go beyond a pre-specified safety limit. Often times the operator has little or no opportunity to react since a fast scram signal shuts down the reactor smoothly and efficiently. These accidents are of interest to technical support personnel since examining the conditions that gave rise to these situations help determine causality. In many other cases an automated fault-diagnostic advisor would be a valuable tool in assisting the technicians and operators to determine what just happened and why.

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