基于改进的SENet航空发动机振动预测

为实时监测和预警航空发动机振动状态，基于气路及振动参数，提出一种使用改进的SENet（squeeze-and-excitation network）模型，对航空发动机近未来的振动进行预测。该研究相比以往采用的实验室模拟数据和仿真数据，使用了真实的QAR（quick access recorder）数据并进行随机采样，以求更能表征发动机振动和工作参数之间的关系。同时，不仅使用其他振动信号进行验证，还在其他型号的发动机上进行测试。结果表明:针对航空发动机的振动进行预测是可行的，SENet模型可以有效并实时追踪振动的突变和波动。此外，该方法对于其他振动信号和不同类型的发动机具有一定的适用性。而且相较于以往采用的其他经典的深度模型，SENet模型在振动的预测中能得到更小的误差。实验证明，相较于以往只使用振动这个单参数进行预测，并行使用与振动相关的多参数融合进行研究更能提高预测的准确性。 

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.