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

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

Moradi, M. (TU Delft Structural Integrity & Composites)
Chiachío, Juan (Universidad de Granada; University of Granada)
Zarouchas, D. (TU Delft Structural Integrity & Composites)

2023

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.

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

http://resolver.tudelft.nl/uuid:d3912853-0178-40ba-9735-d3699107c9cf

https://doi.org/10.7712/150123.9844.451295

2024-01-08

Proceedings of the 10th ECCOMAS Thematic Conference on Smart Structures and Materials, 10

10th ECCOMAS Thematic Conference on Smart Structures and Materials, 2023-07-03 → 2023-07-05, Patras, Greece

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.

Institutional Repository

conference paper

© 2023 M. Moradi, Juan Chiachío, D. Zarouchas