Results of the GVT of the Unmodified GARTEUR SM-AG-19 Testbed in South America

Rett, Sandro R.; Nabarrete, Airton; Arbelo, Mariano A.; Góes, Luiz Carlos Sandoval; Guimarães, Gustavo Paulinelli

doi:10.2514/6.2010-2808

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…To obtain the data for model training and evaluation, in this paper, a self-built GAR-TEUR aircraft model is utilized for data collection. The GARTEUR aircraft model is a typical and widely used [34][35][36][37] standard aircraft model with high compliance, low frequency, and dense frequency characteristics. It is designed to evaluate the accuracy and validity for modal testing methods.…”

Section: Data Collectionmentioning

confidence: 99%

Random Dynamic Load Identification with Noise for Aircraft via Attention Based 1D-CNN

Zhang

Feng

et al. 2022

Aerospace

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Dynamic load identification plays an important role in the field of fault diagnosis and structural modification design for aircraft. In conventional dynamic load identification approaches, accurate structural modeling is usually needed, which is difficult to obtain for highly nonlinear or unknown structures. In this paper, a one-dimensional convolution neural network with multiple modules is proposed for random dynamic load identification of aircraft. Firstly, the convolution module is designed for temporal feature extraction. Secondly, the extracted features are linearly weighted based on the contributions to the final output. The contributions are learned in a data driven manner via the designed attention module. Lastly, the dynamic load of a certain time stamp is predicted from the learned and weighted features. The proposed model is trained and tested using the real data from a GARTEUR aircraft model. Extensive experimental results with qualitative and quantitative evaluations have demonstrated the identification performance with satisfactory accuracy of the proposed approach under different strengths of load noises.

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Section: Data Collectionmentioning

confidence: 99%