Bayesian Belief Networks for Fault Identification in Aircraft Gas Turbines

Abstract

This paper describes the methodology for usage of Bayesian Belief Networks (BBNs) in fault detection for aircraft gas turbine engines. First, the basic theory of BBNs is discussed, followed by a discussion on the application of this theory to a specific engine. In particular, the selection of faults and the means by which operating regions for the BBN system are chosen are analyzed. This methodology is then illustrated using the GE CFM56-7 turbofan engine as an example.

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Document Details

Document Type
Technical Report
Publication Date
Jun 15, 2000
Accession Number
ADA378859

Entities

People

  • Aaaron T. Reed

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Masses
  • Aircrafts
  • Computational Science
  • Engineering
  • Engines
  • Equations
  • Expert Systems
  • Gas Turbines
  • High Pressure
  • Identification
  • Identification Systems
  • Jet Engines
  • Mathematical Models
  • Mechanical Engineering
  • Reasoning
  • Turbines
  • Turbofan Engines

Readers

  • Aerospace Engineering
  • Computational Linguistics
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference