Neural Network Inverse Models for Propulsion Vibration Diagnostics
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 engine, 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.
Recommended Citation
H. Huang et al., "Neural Network Inverse Models for Propulsion Vibration Diagnostics," Proceedings of SPIE - The International Society for Optical Engineering, vol. 4390, pp. 12 - 21, Society of Photo-optical Instrumentation Engineers, Jan 2001.
The definitive version is available at https://doi.org/10.1117/12.421182
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Extended Kalman filter; Inverse model; Neural network; Propulsion diagnostics; Support vector machine
International Standard Serial Number (ISSN)
0277-786X
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 Society of Photo-optical Instrumentation Engineers, All rights reserved.
Publication Date
01 Jan 2001
