Using a Neural Network and a Statistical Classifier for Aircraft Fault Diagnostics.

Abstract

In recent years, several techniques have been developed to create intelligent diagnostic aiding systems. Most of these systems, including the current Integrated Maintenance Information System (IMIS) diagnostics module, involve modeling the systems to be maintained. These systems have the disadvantage of requiring extensive efforts to develop them. A developing technology, neural networks, provides a promising alternative. Neural nets develop diagnostics strategies by learning from past experience with the system, and do not require extensive modeling. Neural networks are well suited to diagnostics applications. This paper presents: A detailed description of a neural network based diagnostic system; An explanation of a radial basis function (RBF) neural network architecture and its construction; An explanation of the construction of a statistical classifier; A description of data representation and method of system optimization; and Performance and experimental result.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1996
Accession Number
ADA319499

Entities

People

  • Shing P. Chu

Organizations

  • Armstrong Laboratory

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Aircrafts
  • Computing System Architectures
  • Construction
  • Data Analysis
  • Data Science
  • Governments
  • Information Systems
  • Learning
  • Logistics
  • Machine Learning
  • Maintenance
  • Network Architecture
  • Neural Networks
  • Optimization
  • Three Dimensional

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Computer Networking
  • Instructional Design and Training Evaluation.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks