A semiautomatic approach to deriving turbine generator diagnostic knowledge

M. Todd, S.D.J. McArthur, J.R. McDonald, S.J. Shaw

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

Condition monitoring of turbine generators, housed at British Energy nuclear power stations throughout the U.K., is implemented to diagnose incipient faults at an early stage, so corrective action can be taken to avoid the associated high costs of an unplanned shutdown. A prototype expert system has been developed that provides decision support to condition monitoring experts who monitor British Energy turbine generators. The expert system automatically interprets data from strategically positioned sensors and transducers on the turbine generator by applying expert knowledge in the form of heuristic rules. This paper reviews the application domain and describes the work undertaken in developing the prototype expert system. The paper also outlines a learning module design that uses an approach based on an analytical symbolic machine learning technique, explanation-based generalization, to semiautomatically derive heuristic rules for turbine generator fault diagnosis. The approach adopted by the learning module is explained in detail and a worked example demonstrates how the learning module can derive a fault heuristic from a single training example. The modular approach to capturing the causal fault and behavioral models is described, and the method in which the module will be integrated with the existing expert system has been outlined. A preliminary evaluation of the learning module design is discussed.
Original languageEnglish
Pages (from-to)979-992
Number of pages13
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Volume37
Issue number5
DOIs
Publication statusPublished - Sep 2007

Keywords

  • expert systems
  • fault diagnosis
  • knowledge acquisition
  • knowledge-based systems
  • learning systems
  • power system monitoring

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