A Bayesian Classifier Based on a Deterministic Annealing Neural Network for Aircraft Fault Classification.

Abstract

A Bayesian classifier based on a recurrent neural network was developed for aircraft fault classification. From historical maintenance data the posterior probabilities of fault classification based on given fault indicators are estimated and derived using the Bayes' rule. Based on Bayesian decision theory, the fault classification problem is formulated as a linear integer programming problem to minimize an expected loss function using the posterior probabilities. The linear integer programming problem is then converted equivalently to a standard linear programming problem. A two layer recurrent neural network is used to carry out the computation task for fault classification by solving the formulated linear programming problem. The simulation results of a pilot study based on the synthetic data on the fire control radar system in F-16 aircraft show that the neural network approach is capable of real-time aircraft fault classification.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1997
Accession Number
ADA323742

Entities

People

  • Jun Wang
  • Shing P. Chu

Organizations

  • Armstrong Laboratory

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Aircrafts
  • Annealing
  • Artificial Intelligence Software
  • Computer Programming
  • Convex Programming
  • Decision Theory
  • Information Systems
  • Integer Programming
  • Linear Programming
  • Machine Learning
  • Maintenance
  • Neural Networks
  • Pilot Studies
  • Radar
  • Recurrent Neural Networks
  • Standards

Readers

  • Neural Network Machine Learning.
  • Operations Research
  • Parallel and Distributed Computing.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks