Diagnosis of Analog Electronic Circuits: A Functional Approach.

Abstract

This describes and proposes an analog electronic circuit diagnostic system using functional knowledge and an artificial intelligence expert system. A prototype of the system is implemented using the KEE expert system building tool to demonstrate its applicability. Functional knowledge is used to analyze the circuit instead of just the structural information widely used now. This permits a more specific identification of bad components within a circuit. The current system in use at Warner Robins Air Logistic Center returns lists of bad components ranging in length up to 25 and 30 individual parts. Functional reasoning will enable the system to further restrict the specification of bad parts. Research into the current literature provides the background and basic knowledge needed to determine the required information for the diagnostic system knowledge base. The system described in this paper proposes the use of a functional rule knowledge base in addition to a structural data base to perform a more complete diagnosis than is now done by the system in use at some Air Force logistic centers.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1986
Accession Number
ADA172718

Entities

People

  • Donald R. Wunz Jr

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • C4I
  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Circuit Analysis
  • Circuit Boards
  • Circuits
  • Computer Programming
  • Computer Science
  • Databases
  • Electronic Circuits
  • Engineering
  • Engineers
  • Expert Systems
  • Failure Mode And Effect Analysis
  • Plastic Explosives
  • Reasoning
  • Waveforms

Readers

  • Artificial Intelligence
  • Computer Science/Computer Engineering/Data Science/Digital Signal Processing.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • Microelectronics