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

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