Title

Propulsion Vibration Analysis Using Neural Network Inverse Modeling

Abstract

Neural networks are employed to predict the amount and location of propulsion system rotor unbalance. Vibration data used to train and test inverse system models are generated via a high-order structural dynamic finite element model. Several neural network methods, including feed forward neural network using back propagation, node-decoupled Kalman filter (NDEKF) and support vector machines (SVMs) are investigated. Training results and performance among the various methods are compared. Original applications to nonlinear structural models and damaged structure models are shown.

Meeting Name

2002 International Joint Conference on Neural Networks (IJCNN '02) (2002: May 12-17, Honolulu, HI)

Department(s)

Electrical and Computer Engineering

International Standard Serial Number (ISSN)

1098-7576

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2002 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.


Share

 
COinS