Accession Number:

ADA393234

Title:

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

Descriptive Note:

Technical memo.

Corporate Author:

ARMY RESEARCH LAB CLEVELAND OH*

Report Date:

2001-07-01

Pagination or Media Count:

16.0

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.

Subject Categories:

  • Aircraft
  • Electrical and Electronic Equipment
  • Numerical Mathematics
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE