A novel neural network based on dynamic time warping and Kalman filter for real-time monitoring of supersonic inlet flow patterns

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 its flow patterns, so it plays a crucial part and is necessary to develop methods for monitoring its flow patterns to ensure stable and safe operation. This issue can be viewed as a time series classification (TSC) task. Traditionally, several manually-engineered features are extracted as the indicators to evaluate the operation status, but this process can be heavily dependent on the professional experience. In this paper, a novel neural network called DTW-SLFN-KF is proposed, which integrates Dynamic Time Warping (DTW) and Kalman Filter (KF) into a single-hidden-layer neural network (SLFN) architecture to directly determine flow patterns from the dynamic sensor signals. The proposed network first adopts a DTW layer as the feature extractor to automatically extract robust features, and exploits the flexible alignment ability of DTW to keep the temporal continuity and deal with the temporal distortions. Then, these features are fed into an SLFN for classification. After that, to make full use of the extracted features and improve the classification performance of SLFN when the network structure is fixed, KF is applied as a linear post-processing technique to get the predicted output of SLFN closer to the true output. Experimental results demonstrate that the proposed DTW-SLFN-KF network has better comprehensive performance for monitoring the flow patterns of supersonic inlet in terms of monitoring accuracy and real-time performance when compared with other competitive methods.