Supersonic air inlet flow state monitoring method based on distinguishing feature learning

Abstract

The invention provides a method for monitoring the flow state of a supersonic air inlet channel, which adopts a method of combining a time-frequency analysis technology and deep learning to monitor the flow state of the supersonic air inlet channel from a dynamic sensor signal. The method comprises the following steps: performing preliminary signal processing on a dynamic sensor signal by using continuous wavelet transform, converting the dynamic sensor signal into a time-frequency spectrogram, and then inputting the time-frequency spectrogram into a convolutional neural network (CNN) for classification. In order to reduce the classification error, the invention provides a DDL-CNN / TDL-CNN (Doublet / Triplet ConvolutionalNeural Network combined with Discriminative Learning) which simultaneously considers the loss of the cross entropy and the learning of the discriminative features. The proposed method encourages the convolution module to project the time-frequency spectrogram to a feature space, so that different flow states in the space become more separable. Experimental results show that compared with a traditional CNN, the DDL-CNN / TDL-CNN has better performance on multiple indexes.

Description

technical field [0001] The invention belongs to the technical field of monitoring the flow state of a supersonic inlet, and in particular relates to a method for monitoring the flow state of a supersonic inlet. Background technique [0002] As one of the key components of the air-breathing supersonic propulsion system, the supersonic inlet has a direct impact on the performance of the whole system. For example, supersonic inlet surge is an extremely unstable flow state, usually accompanied by severe and periodic shock wave oscillations, which will seriously deteriorate the thrust characteristics of the entire propulsion system, and is always facing the risk of flameout . Not only that, but the periodic thermal load generated by the shock wave oscillation will also make the controllability of the whole system deteriorate sharply, and may even directly lead to the destruction of the overall structure, posing a threat to the safety of the aircraft and personnel. From the pers...

AI Technical Summary

Problems solved by technology

Therefore, in the previous research process, the dynamic characteristics in the actual test data and the influence of factors such as noise and interference will be ignored.

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