Advanced Health Monitoring of Composite Structures Through Deep Learning-Based Analysis of Lamb Wave Data for Developing Health Indicators

Conference paper (2023)

Authors

M. Moradi Structural Integrity & Composites - Aerospace Engineering
F.C. Gul Structural Integrity & Composites - Aerospace Engineering
Juan Chiachío Universidad de Granada, University of Granada
R. Benedictus Structural Integrity & Composites - Aerospace Engineering
D. Zarouchas Structural Integrity & Composites - Aerospace Engineering
Research Group
Structural Integrity & Composites (Aerospace Engineering) (TU Delft)
Copyright: © 2023 M. Moradi, F.C. Gul, Juan Chiachío, R. Benedictus, D. Zarouchas
DOI
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https://doi.org/10.12783/shm2023/36862
Signal Processing Deep learning (DL) Composite structures Semi-supervised learning Structural health monitoring (SHM) Guided waves Intelligent health indicator Prognostics and health management (PHM) Tension-Tension fatigue Machine learning (ML) algorithms
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Published Date

2023

Language

English

Reuse Rights

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Faculty

Aerospace Engineering

Department

Aerospace Structures & Materials

Research Group

Structural Integrity & Composites

Abstract

A health indicator (HI) serves as an intermediary link between structural health monitoring (SHM) data and prognostic models, and an efficient HI should meet prognostic criteria, i.e., monotonicity, trendability, and prognosability. However, designing a proper HI for composite structures is a challenging task due to the complex damage accumulation process during operational conditions. Additionally, designing a HI that is independent of historical SHM data (i.e., from the healthy state until the current state) is even more challenging as HI and remaining useful life prediction are time-dependent phenomena. A reliable SHM technique is required to extract informative time-independent data, and a powerful model is necessary to construct a proper HI from that data. The lamb wave (LW) technique is a useful SHM method that can extract such time-independent data. However, translating the LW data at each time step to the appropriate HI value
is a challenge. AI—deep learning in this case—offers significant mathematical potential to meet this difficulty. A semi-supervised learning approach is developed to train the model using the simulated ideal HIs as the targets. The model uses the current LW data, without prior or subsequent data, to output the current HI value. Prognostic criteria are then calculated using the entire HI trajectory until the end-of-life. To validate the proposed approach, aging experiments from NASA’s prognostics data repository are used, which include composite specimens subjected to a tension-tension fatigue loading and monitored using the LW technique. The LW data is first processed using the Hilbert transform, and then, upper and lower signal envelopes in two states – baseline and current time – are used to feed the deep learning model. The results confirm the effectiveness of the proposed approach according to the prognostic criteria. The effect of different triggering frequencies of the LW system on the results is also discussed in terms of the prognostic criteria.