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Proceedings Paper

Neural network inverse models for propulsion vibration diagnostics
Author(s): Haiying Huang; John L. Vian; Jai Choi; David Carlson; Donald C. Wunsch
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Paper Abstract

Neural network based inverse modeling approach is investigated to predict propulsion system rotor unbalance. The frequency response of vibration collected from an engine model is used as inputs to train neural networks, which identify the source of unbalance and determine the amount of rotor unbalance. High-order finite-element structural dynamic models of airplane engines, case, nacelle, and strut are used to produce training/testing data. Performance of several neural networks inverse models, including back- propagation, extended Kalman filter, and support vector machine, are compared. The ability to locate and quantify unbalance source with respect to multiple engine fan and turbine stages is demonstrated.

Paper Details

Date Published: 21 March 2001
PDF: 10 pages
Proc. SPIE 4390, Applications and Science of Computational Intelligence IV, (21 March 2001); doi: 10.1117/12.421182
Show Author Affiliations
Haiying Huang, Univ. of Missouri/Rolla (United States)
John L. Vian, Boeing Co. (United States)
Jai Choi, Boeing Co. (United States)
David Carlson, Boeing Co. (United States)
Donald C. Wunsch, Univ. of Missouri/Rolla (United States)


Published in SPIE Proceedings Vol. 4390:
Applications and Science of Computational Intelligence IV
Kevin L. Priddy; Paul E. Keller; Peter J. Angeline, Editor(s)

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