Vibration prediction of aeroengines based on enhanced SENet model

In order to monitor the vibration status of aeroengines and acquire warning signals in real-time, an enhanced SENet (squeeze-and-excitation network) model was proposed based on gas path and vibration parameters. Compared with the previous research which used datasets generated from specific lab situations and simulation data, actual QAR （quick access recorder） data were adopted for random sampling of the datasets. This technique could characterize the real operation status and the interaction of parameters better in vibration systems. The results showed that it is possible to forecast the vibration of aeroengines, and the SENet model could effectively and timely track sudden changes and the fluctuation of vibration. In addition, the applicability of this method into other vibration parameters and different types of aeroengines was tested. Furthermore, compared with other classical learning algorithms, the SENet model may obtain a smaller error in vibration forecasting. At the same time, the experiments showed that compared with previous research only focusing on the vibration, using the fusion of multi parameters could improve the accuracy of the forecast. © 2022 BUAA Press. All rights reserved.