Plant control expert system coping with unforeseen events—model based reasoning using fuzzy qualitative reasoning

ABSTRACT

An ordinary expert system controls a plant according to heuristics. So, it fails to control the plant for lack of heuristics if unforeseen events occur as a result of abnormal situations. We propose a new framework of model-based reasoning that can dynamically generate the knowledge for plant control against unforeseen events. This proposed framework consists of three functions: (a) generation of the goal state after recovery from the unforeseen events; (b) generation of knowledge for plant control; (c) prediction of process trend curves and estimation of the generated knowledge. In the proposed framework, various kinds of models which correspond to the fundamental knowledge about plant control are used. We have implemented a thermal power plant control expert system on the basis of this proposed framework. This paper describes the model-based reasoning mechanism of the experimental plant control expert system to realize each of three functions. Especially as for (c), this paper explains qualitative reasoning mechanism using fuzzy logic.

REFERENCES

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.1 Applications and Expert Systems
          Subjects: Industrial automation

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.3 Deduction and Theorem Proving
          Subjects: Uncertainty, "fuzzy," and probabilistic reasoning
      I.2.6 Learning
          Subjects: Knowledge acquisition

  J. Computer Applications
  J.7 COMPUTERS IN OTHER SYSTEMS
      Subjects: Command and control

General Terms:
Algorithms, Design, Theory