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.
Recommended Citation
X. Hu et al., "Propulsion Vibration Analysis Using Neural Network Inverse Modeling," Proceedings of the International Joint Conference on Neural Networks, vol. 3, pp. 2866 - 2871, Institute of Electrical and Electronics Engineers (IEEE), Jan 2002.
The definitive version is available at https://doi.org/10.1109/IJCNN.2002.1007603
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.
Publication Date
01 Jan 2002
