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Hybrid Neural-Network: Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics Developed and DemonstratedAs part of the NASA Aviation Safety Program, a unique model-based diagnostics method that employs neural networks and genetic algorithms for aircraft engine performance diagnostics has been developed and demonstrated at the NASA Glenn Research Center against a nonlinear gas turbine engine model. Neural networks are applied to estimate the internal health condition of the engine, and genetic algorithms are used for sensor fault detection, isolation, and quantification. This hybrid architecture combines the excellent nonlinear estimation capabilities of neural networks with the capability to rank the likelihood of various faults given a specific sensor suite signature. The method requires a significantly smaller data training set than a neural network approach alone does, and it performs the combined engine health monitoring objectives of performance diagnostics and sensor fault detection and isolation in the presence of nominal and degraded engine health conditions.
Document ID
20050204000
Acquisition Source
Glenn Research Center
Document Type
Other
Authors
Kobayashi, Takahisa (QSS Group, Inc. United States)
Simon, Donald L. (Department of the Army United States)
Date Acquired
September 8, 2013
Publication Date
March 1, 2002
Publication Information
Publication: Research and Technology 2001
Subject Category
Aircraft Propulsion And Power
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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