A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics

Abstract

In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2001
Accession Number
ADA393234

Entities

People

  • Donald L. Simon
  • Takahisa Kobayashi

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Human Systems
  • Sensors

DTIC Thesaurus Topics

  • Aircraft Engines
  • Aircrafts
  • Algorithms
  • Databases
  • Detection
  • Detectors
  • Engines
  • False Alarms
  • Gas Turbines
  • Genetic Algorithms
  • High Pressure
  • Jet Engines
  • Measurement
  • Neural Networks
  • Turbines
  • Turbofan Engines
  • Warning Systems

Readers

  • Computational Modeling and Simulation
  • Neural Network Machine Learning.
  • Pulsed Power and Plasma Physics.

Technology Areas

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