Gas compressor rotation stall early warning method based on time expansion convolutional network

Abstract

A gas compressor rotation stall early warning method based on a time expansion convolutional network comprises the following steps: firstly, preprocessing dynamic pressure data of an aero-engine, and dividing a test data set and a training data set from experimental data; secondly, sequentially constructing a time convolutional network module, constructing a Resnet-v network module, constructing a time expansion convolutional network prediction model, and storing an optimal prediction model; finally, performing real-time prediction on the test data: firstly, adjusting the data dimension of the test set according to the input requirement of the time convolutional network prediction model; according to a time sequence, calculating a surge prediction probability of each sample through a time expansion convolutional network prediction model; and calculating the real-time surge probability of a pair of samples containing covariables and not containing covariables through a time expansion convolutional network prediction model, and observing the improvement effect of the covariables on the model prediction effect. The time domain statistical characteristics and the change trend are integrated, and the prediction precision is improved; and the active control performance of the engine can be improved, and certain universality is achieved.

Description

Technical field [0001] The present invention relates to a method of rotating atmospheric warning method based on time expansion convolutionary network, belonging to aerospace engine modeling and simulation technology. Background technique [0002] The performance stability of the avionics is directly related to the flight safety of the whole machine, and the air circuit components maintain the overall work state of the engine, in various common air circuit faults, the compressor rotates Stalling is one of the destructive, very fast failures, so precise identification and timely warning are the focus of domestic and foreign air engine fields. In general, the development of the irridity of the compressor is mainly steady state, atmospheric, rotation, stall, and astigmatism, each stage has different characteristics, and the mechanism is more complicated. It is very rapid. When the stable operation, the flow rate is reduced, and the pressure ratio is increased. When the flow rate is ...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the problems of low accuracy and poor reliability in the prior art, the present invention provides a compressor rotation stall early warning method based on time-expanded convolutional network

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