Developing health indicators for composite structures based on a two-stage semi-supervised machine learning model using acoustic emission data
M. Moradi (Structural Integrity & Composites)
Juan Chiachío (Universidad de Granada, University of Granada)
D. Zarouchas (Structural Integrity & Composites)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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.