21 March 2001 Neural network inverse models for propulsion vibration diagnostics
Author Affiliations +
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.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Haiying Huang, John L. Vian, Jai Choi, David Carlson, Donald C. Wunsch, "Neural network inverse models for propulsion vibration diagnostics", Proc. SPIE 4390, Applications and Science of Computational Intelligence IV, (21 March 2001); doi: 10.1117/12.421182; https://doi.org/10.1117/12.421182
PROCEEDINGS
10 PAGES


SHARE
Back to Top