Joint discriminative learning and classification for monitoring flow patterns of supersonic inlet

Abstract

A supersonic inlet is one of the key components in a supersonic air-breathing propulsion system and is the basis for protection control. The overall system performance can be greatly influenced by inlet flow patterns, so it's necessary to develop methods for monitoring them to ensure stable and safe operation. In this paper, time-frequency analysis and deep learning are combined to determine inlet flow patterns from the dynamic sensor signals. Continuous Wavelet Transform is first used for preliminary signal processing by converting the dynamic sensor signals into the 2-D time-frequency spectrogram, and then the spectrogram is taken as input of Convolutional Neural Network (CNN) for classification. In order to reduce the classification error, Doublet/Triplet Convolutional Neural Network combined with Discriminative Learning (DDL-CNN/TDL-CNN) are proposed that take both the cross entropy loss and discriminative learning of features into account simultaneously. The proposed methods encourage CNN to map the spectrogram into a feature space where different flow patterns become more separable. The experimental results demonstrate that DDL-CNN/TDL-CNN have better performance for monitoring the flow patterns of supersonic inlet. Compared with the baseline CNN, three multi-classification metrics of DDL-CNN/TDL-CNN are higher on most of 31 sensors, and under the statistical comparisons, the performance of DDL-CNN/TDL-CNN is also remarkably better. From the two-dimensional representations obtained by t-sne, different flow patterns become more separable for DDL-CNN/TDL-CNN after taking the constraints of discriminative learning into consideration.