Propulsion vibration analysis using neural network inverse modeling

被引：0

作者：

Hu, X ^{[1
]}

Vian, J ^{[1
]}

Choi, J ^{[1
]}

Carlson, D ^{[1
]}

Wunsch, DC ^{[1
]}

机构：

[1] Boeing Phantom Works, Seattle, WA 98124 USA

来源：

PROCEEDING OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-3 | 2002年

关键词：

D O I：

10.1109/IJCNN.2002.1007603

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 highorder 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.

引用

页码：2866 / 2871

页数：2

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