A data driven methodology for upscaling remaining useful life predictions

A data driven methodology for upscaling remaining useful life predictions: From single- to multi-stiffened composite panels

Galanopoulos, Georgios (University of Patras)
Fytsilis, Efthimios (University of Patras)
Yue, N. (TU Delft Structural Integrity & Composites)
Broer, Agnes A.R. (TU Delft Structural Integrity & Composites)
Milanoski, Dimitrios (University of Patras)
Zarouchas, D. (TU Delft Structural Integrity & Composites)
Loutas, Theodoros (University of Patras)

2023

In this paper we execute a complex test campaign to develop a novel methodology for the Remaining Useful Life (RUL) estimation of complex multi-stiffened composite aeronautical panels utilizing Machine Learning models trained with Structural Health Monitoring (SHM) data from hierarchically simpler elements, i.e., single-stiffened panels. Distributed Fiber Optical sensors (DFOS) are employed to monitor the panels’ behavior undergoing variable amplitude compression-compression fatigue after multiple impacts. A data processing methodology is first applied to the DFOS data, to both alleviate the effect of the variable loading conditions on the monitored strain and ease the computational burden. In this upscaling endeavor, an advanced strain-based Health Indicator (HI) based on Genetic algorithms, created and validated on the single-stiffened panel data, is utilized as the prognostic feature for the RUL estimations of the multi-stiffened panels. The HI displays favorable characteristics in terms of monotonicity and prognosability which are highly desirable for more accurate RUL estimations. For the prognostic task, standard machine learning models are trained using the historical degradation data of the single-stiffened panels and a similarity analysis is performed to enhance the accuracy when predicting the RUL of the multi-stiffened panels. Despite the increased structural complexity of the multi-stiffened panels, we demonstrate that the RUL is able to be predicted with reasonable accuracy. The present work paves the road for upscaling and applying prognostic methodologies to more complex structures beyond simple coupons or generic elements.

Composite structures
Compression fatigue
Fiber optical sensors
Health Indicators
Remaining useful life prediction
Stiffened panels
Structural health monitoring

http://resolver.tudelft.nl/uuid:19747d84-1eb6-47d4-8511-d2b5d9a9221b

https://doi.org/10.1016/j.jcomc.2023.100366

2666-6820

Composites Part C, 11

Institutional Repository

journal article

© 2023 Georgios Galanopoulos, Efthimios Fytsilis, N. Yue, Agnes A.R. Broer, Dimitrios Milanoski, D. Zarouchas, Theodoros Loutas