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

Abstract

Loads and strains in critical areas play a crucial role in aircraft structural health monitoring, the tracking of individual aircraft lifespans, and the compilation of load spectra. Direct measurement of actual flight loads presents challenges. This process typically involves using load-strain stiffness matrices, derived from ground calibration tests, to map measured flight parameters to loads at critical locations. Presently, deep learning neural network methods are rapidly developing, offering new perspectives for this task. This paper explores the potential of deep learning models in predicting flight parameter loads and strains, integrating the methods of flight parameter preprocessing techniques, flight maneuver recognition (FMR), virtual ground calibration tests for wings, dimensionality reduction of flight data through Autoencoder (AE) network models, and the application of Long Short-Term Memory (LSTM) network models to predict strains. These efforts contribute to the prediction of strains in critical areas based on flight parameters, thereby enabling real-time assessment of aircraft damage.

Keywords

• maneuver flight recognition (MFR)
• autoencoder (AE) neural network
• flight parameter dimensionality reduction
• long short-term memory neural network (LSTM)
• strain prediction