Neural network inverse models for propulsion vibration diagnostics

Haiying Huang; John L. Vian; Jai Choi; David Carlson; Donald C. Wunsch II

doi:10.1117/12.421182

21 March 2001 Neural network inverse models for propulsion vibration diagnostics

Haiying Huang, John L. Vian, Jai Choi, David Carlson, Donald C. Wunsch II

Proceedings Volume 4390, Applications and Science of Computational Intelligence IV; (2001) https://doi.org/10.1117/12.421182
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

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.

Citation Download Citation

Haiying Huang, John L. Vian, Jai Choi, David Carlson, and Donald C. Wunsch II "Neural network inverse models for propulsion vibration diagnostics", Proc. SPIE 4390, Applications and Science of Computational Intelligence IV, (21 March 2001); https://doi.org/10.1117/12.421182

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