Stall prediction model based on deep learning network in axial flow compressor

To predict stall and surge in advance that make the aero-engine compressor operate safely, a stall prediction model based on deep learning theory is established in the current study. The Long Short-Term Memory (LSTM) originating from the recurrent neural network is used, and a set of measured dynamic pressure datasets including the stall process is used to learn what determines the weight of neural network nodes. Subsequently, the structure and function hyperparameters in the model are deeply optimized, and a set of measured pressure data is used to verify the prediction effects of the model. On this basis of the above good predictive capability, stall in low- and high-speed compressor are predicted by using the established model. When a period of non-stall pressure data is used as input in the model, the model can quickly complete the prediction of subsequent time series data through the self-learning and prediction mechanism. Comparison with the real-time measured pressure data demonstrates that the starting point of the predicted stall is basically the same as that of the measured stall, and the stall can be predicted more than 1 s in advance so that the occurrence of stall can be avoided. The model of stall prediction in the current study can make up for the uncertainty of threshold selection of the existing stall warning methods based on measured data signal processing. It has a great application potential to predict the stall occurrence of aero-engine compressor in advance and avoid the accidents.