Application of Neural Networks to the F/A-18 Engine Condition Monitoring System

Abstract

Neural networks were applied to the Engine Condition and Monitoring System of the F/A-18 aircraft. Due to recent fleet experience with compressor blade failures in flight, neural networks were applied to three engine conditions, flameout due to compressor failures, normal operating conditions, and low oil pressure conditions. An attempt was made to predict compressor failure using the neural networks. A back propagation and back propagation/ Kohonen network were successfully tested in recognizing the various conditions with data previously unseen by the networks. Both networks demonstrated promise in predicting failures although not enough data was available for conclusive results. Keywords: Theses; Data acquisition; Fortran; Naval aircraft.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1989
Accession Number
ADA219820

Entities

People

  • Joseph T. Gengo

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Air Platforms
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Acquisition
  • Aeronautical Engineering
  • Aircrafts
  • Algorithms
  • Compressor Blades
  • Compressors
  • Computer Programming
  • Computer Programs
  • Computers
  • Data Acquisition
  • Data Sets
  • Mainframe Computers
  • Pattern Recognition
  • Recognition
  • Training
  • Turbines
  • United States

Readers

  • Aerospace Engineering
  • Neural Network Machine Learning.
  • Structural Health Monitoring of Composite Structures.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks