Research on Engine Thrust and Load Factor Prediction by Novel Flight Maneuver Recognition Based on Flight Test Data

Abstract

Flight maneuver recognition (FMR) is a critical tool for capturing essential information about the state of an aircraft, which is necessary to improve pilot training, flight safety, and autonomous air combat. However, due to the alignment of multidimensional, multimodal time series and insufficient data, challenges exist that limit the accuracy of FMR. In this paper, two FMR methods, including an improved dynamic time-warping distance-based algorithm (D-DTW) and a perceptually important point-based method, are proposed based on time series mining techniques. The differential dynamics equations of the aircraft’s center of gravity in the trajectory coordinate system are established. Subsequently, based on the obtained flight data, the engine thrust is derived by employing criteria based on flight mechanics and coordinate system transformation methods. Finally, the flight profile is clustered and divided based on the preprocessed data. The engine load factor is obtained through centroid transformation and coordinate system translation based on flight dynamics calculations. The results indicate that the two methods exhibit varying applicability with respect to FMR. However, the second method is more suitable regarding the recognition or prediction of engine thrust and load factor.

The Research on Deep Learning-Driven Dimensionality Reduction and Strain Prediction Techniques Based on Flight Parameter Data

“…(a) Schematic diagram of the wing finite element method (FEM): This diagram illus trates the structural analysis of the wing using the finite element method, which is a com putational tool for predicting the behavior of the wing under various loading conditions The engine loads were not directly simulated, because the engine layout of this type o Details can be found in our previous work [26].…”