An ensemble radius basis function network based on dynamic time warping for real-time monitoring of supersonic inlet flow patterns

Abstract As the basis of protection control, supersonic inlet plays an important role in a supersonic air-breathing propulsion system, so it is of great significance to ensure the safe and stable operation by monitoring its flow patterns. From the perspective of machine learning, 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, which can be heavily dependent on the professional experience and time-consuming. This paper proposes a novel Dynamic Time Warping-Radius Basis Function (DTW-RBF) network to directly determine the flow patterns from the dynamic input signals. DTW-RBF network replaces the Euclidean distance in the static RBF kernels with the DTW distance, which exploits the elastic matching ability of DTW to align the input signals to the kernels. Then, the second-order Levenberg-Marquarelt (LM) optimization algorithm is used to allow the efficient training process of the proposed network. In order to determine the appropriate locations for sensor placement and enhance the robustness and reliability of monitoring flow patterns by a single sensor, an optimal subset of sensors is further selected for ensemble through multi-objective optimization and fuzzy decision. Experimental results demonstrate that the proposed DTW-RBF network works efficiently for TSC tasks on benchmark time series datasets, and has better comprehensive performance for monitoring supersonic inlet flow patterns in terms of classification accuracy and test time. The ensemble classifier further increases the classification accuracy with still meeting the real-time requirements.