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A multi-model architecture based on deep learning for aircraft load prediction

Engineering and Technology

A multi-model architecture based on deep learning for aircraft load prediction

C. Sun, H. Li, et al.

This research presents a groundbreaking deep learning-based aircraft load model that achieves remarkable prediction accuracy and goodness-of-fit using extensive flight data. The study, conducted by authors from Peking University and the Aviation Industry Corporation of China, significantly enhances strain prediction and load model calibration, ultimately reducing the need for ground testing.

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Playback language: English
Abstract
Monitoring aircraft structural health with changing loads is critical. This paper presents a deep learning-based aircraft load model for strain prediction and load model calibration using a two-phase process. The model achieves 97.16% prediction accuracy and 99.49% goodness-of-fit using 2 million flight recording data points. This reduces ground testing needs and improves load prediction accuracy.
Publisher
Communications Engineering
Published On
Jul 18, 2023
Authors
Chenxi Sun, Hongyan Li, Hongna Dui, Shenda Hong, Yongyue Sun, Moxian Song, Derun Cai, Baofeng Zhang, Qiang Wang, Yongjun Wang, Bo Liu
Tags
aircraft structural health
deep learning
strain prediction
load model calibration
flight data
prediction accuracy
ground testing

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