Damage detection of composite materials using data fusion with deep neural networks

Shweta Dabetwar, Stephen Ekwaro-Osire, João Paulo Dias

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

Abstract

Composite materials have enormous applications in various fields. Thus, it is important to have an efficient damage detection method to avoid catastrophic failures. Due to the existence of multiple damage modes and the availability of data in different formats, it is important to employ efficient techniques to consider all the types of damage. Deep neural networks were seen to exhibit the ability to address similar complex problems. The research question in this work is 'Can data fusion improve damage classification using the convolutional neural network?' The specific aims developed were to 1) assess the performance of image encoding algorithms, 2) classify the damage using data from separate experimental coupons, and 3) classify the damage using mixed data from multiple experimental coupons. Two different experimental measurements were taken from NASA Ames Prognostic Repository for Carbon Fiber Reinforced polymer. To use data fusion, the piezoelectric signals were converted into images using Gramian Angular Field (GAF) and Markov Transition Field. Using data fusion techniques, the input dataset was created for a convolutional neural network with three hidden layers to determine the damage states. The accuracies of all the image encoding algorithms were compared. The analysis showed that data fusion provided better results as it contained more information on the damages modes that occur in composite materials. Additionally, GAF was shown to perform the best. Thus, the combination of data fusion and deep neural network techniques provides an efficient method for damage detection of composite materials.

Original languageEnglish
Title of host publicationStructures and Dynamics
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791884225
DOIs
StatePublished - 2020
EventASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition, GT 2020 - Virtual, Online
Duration: Sep 21 2020Sep 25 2020

Publication series

NameProceedings of the ASME Turbo Expo
Volume10B-2020

Conference

ConferenceASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition, GT 2020
CityVirtual, Online
Period09/21/2009/25/20

Keywords

  • Composite materials
  • Convolutional neural network
  • Damage detection
  • Data fusion
  • Deep neural network

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