Developing health indicators for composite structures based on a two-stage semi-supervised machine learning model using acoustic emission data

M. Moradi*, Juan Chiachío, D. Zarouchas

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

Composite structures are highly valued for their strength-to-weight ratio, durability, and versatility, making them ideal for a variety of applications, including aerospace, automotive, and infrastructure. However, potential damage scenarios like impact, fatigue, and corrosion can lead to premature failure and pose a threat to safety. This highlights the importance of monitoring composite structures through structural health monitoring (SHM) and prognostics and health management (PHM) to ensure their safe and reliable operation. SHM provides information on the current state of the structure, while PHM predicts its future behavior and determines necessary maintenance. Health indicators (HIs) play a crucial role in both SHM and PHM, providing information on structural health and behavior, but accurate determination of these indicators can be challenging due to the complexity of material behavior and multiple sources of damage in composite structures. In the present work, a model containing a developed adaptive standardization, a dimension reduction sub-model, a time-independent sub-model, and a time-dependent sub-model is introduced to address this challenge. First, the raw data collected by the acoustic emission technique monitoring composite structures under fatigue loading is processed to provide plenty of statistical features. The extracted features are adaptively standardized according to the available data until the current time. Then, the principal component analysis algorithm is employed to reconstruct a few yet highly informative features out of those statistical features. An artificial neural network is used to regress the principal components to the HI that meets the prognostic criteria. Finally, the last sub-model takes into account the time dependency of HI values during fatigue loading. In comparison to other models, the results show superior performance.
Original languageEnglish
Title of host publicationProceedings of the 10th ECCOMAS Thematic Conference on Smart Structures and Materials
Place of PublicationPatras, Greece
Pages923-934
Number of pages12
Volume10
DOIs
Publication statusPublished - 2023
Event10th ECCOMAS Thematic Conference on Smart Structures and Materials - Patras, Greece
Duration: 3 Jul 20235 Jul 2023
Conference number: 10

Conference

Conference10th ECCOMAS Thematic Conference on Smart Structures and Materials
Abbreviated titleECCOMAS SMART 2023
Country/TerritoryGreece
CityPatras
Period3/07/235/07/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Prognostic and Health Management (PHM)
  • Structural Health Monitoring (SHM)
  • Intelligent health indicator
  • Artificial Intelligence (AI)
  • Composite structures
  • Acoustic Emission
  • Semi-supervised Learning
  • Adaptive standardization
  • Dimension Reduction
  • Bayesian Optimization
  • Deep learning (DL)
  • Fatigue behavior evaluation
  • Fatigue assessment
  • Impact damage

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