An Expert System Solution for the Quantitative Condition Assessment of Electrical Distribution Systems in the United States Air Force

Abstract

Faced with a rapidly decreasing budget, the Air Force is in need of a method to objectively evaluate its aging utility infrastructure assets. This objective evaluation could be used to compare similar facility infrastructure systems for identification of possible problem areas and prioritization of major repair projects. This thesis developed a component model which can be used to objectively evaluate a typical electrical distribution system. The Delphi process was used to gather expert opinions regarding three areas: (1) the critical components which should be included in the model, (2) the relative importance of each selected critical component, and (3) the criteria used to evaluate each of the selected critical components. The model is used to assign a numerical rating ranging from 0 to 100 to each critical component. The condition indices for the critical components are then combined using a relative weighting scheme to arrive at the overall electrical distribution system condition index. The component model was encoded into a computer based expert system shell to provide a smooth user interface and easy update capabilities. The resulting expert system determines component and system condition indices based on user input or available database information.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1991
Accession Number
ADA244270

Entities

People

  • David O. Paine

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Advanced Electronics
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence
  • Business Administration
  • Circuit Analysis
  • Computer Languages
  • Computer Programming
  • Computer Programs
  • Computers
  • Database Management Systems
  • Databases
  • Domain Specific Programming Languages
  • Expert Systems
  • Information Systems
  • Maintenance Management
  • Management Personnel
  • Relational Database Management Systems
  • United States

Readers

  • Artificial Intelligence
  • Life Cycle Cost Analysis
  • Regression Analysis.