On the usage of hybrid 1-D convolutional network and long-short-term-memory network for constant-amplitude multiple-site fatigue damage prediction on aircraft lap joints

Muhammad Ihsan Mas, Mohamad Ivan Fanany, Timotius Devin, Lintang A. Sutawika

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Multiple site fatigue damage is a problem that affects many operators of aging aircraft. The methods currently in place for prediction of such damage are conservative, sensitive to noise and cannot fully account for grain-level material variations, which results in aircrafts being more conservatively designed than they need to be. The authors augmented the dataset of the FAA AR-07/22 report into a sizable body of variable- and constant-amplitude multiple-site fatigue damage sequences. This was done implementing and tuning the algorithm from AFGROW, implementing the plastic zone linkup criteria for crack interaction, as well as adding Gaussian noise at different stages of the computation. The interim model used for predicting the damage is a hybrid 1-D convolution and bidirectional LSTM model, which achieved an average of 171.26% MAPE, and 4.028 MSLE on the interim version of the dataset. A detailed breakdown of the error characteristics and the hyperparameters that have salient effects on the performance of the model are also examined.

Original languageEnglish
Title of host publicationProceedings - 2017 International Conference on Sustainable Information Engineering and Technology, SIET 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages184-189
Number of pages6
ISBN (Electronic)9781538621820
DOIs
Publication statusPublished - 27 Feb 2018
Event2017 International Conference on Sustainable Information Engineering and Technology, SIET 2017 - Batu City, Indonesia
Duration: 24 Nov 201725 Nov 2017

Publication series

NameProceedings - 2017 International Conference on Sustainable Information Engineering and Technology, SIET 2017
Volume2018-January

Conference

Conference2017 International Conference on Sustainable Information Engineering and Technology, SIET 2017
CountryIndonesia
CityBatu City
Period24/11/1725/11/17

Keywords

  • convolutional neural networks
  • long-short-term-memory network
  • multivariate regression
  • structural fatigue

Fingerprint Dive into the research topics of 'On the usage of hybrid 1-D convolutional network and long-short-term-memory network for constant-amplitude multiple-site fatigue damage prediction on aircraft lap joints'. Together they form a unique fingerprint.

Cite this